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 <h1 align="center"><font color="#00006A">Computing Health  
 Expectancies using IMaCh</font></h1>  <h1 align="center"><font color="#00006A">Computing Health
   Expectancies using IMaCh</font></h1>
 <h1 align="center"><font color="#00006A" size="5">(a Maximum  
 Likelihood Computer Program using Interpolation of Markov Chains)</font></h1>  <h1 align="center"><font color="#00006A" size="5">(a Maximum
   Likelihood Computer Program using Interpolation of Markov Chains)</font></h1>
 <p align="center">&nbsp;</p>  
   <p align="center">&nbsp;</p>
 <p align="center"><a href="http://www.ined.fr/"><img  
 src="logo-ined.gif" border="0" width="151" height="76"></a><img  <p align="center"><a href="http://www.ined.fr/"><img
 src="euroreves2.gif" width="151" height="75"></p>  src="logo-ined.gif" border="0" width="151" height="76"></a><img
   src="euroreves2.gif" width="151" height="75"></p>
 <h3 align="center"><a href="http://www.ined.fr/"><font  
 color="#00006A">INED</font></a><font color="#00006A"> and </font><a  <h3 align="center"><a href="http://www.ined.fr/"><font
 href="http://euroreves.ined.fr"><font color="#00006A">EUROREVES</font></a></h3>  color="#00006A">INED</font></a><font color="#00006A"> and </font><a
   href="http://euroreves.ined.fr"><font color="#00006A">EUROREVES</font></a></h3>
 <p align="center"><font color="#00006A" size="4"><strong>March  
 2000</strong></font></p>  <p align="center"><font color="#00006A" size="4"><strong>March
   2000</strong></font></p>
 <hr size="3" color="#EC5E5E">  
   <hr size="3" color="#EC5E5E">
 <p align="center"><font color="#00006A"><strong>Authors of the  
 program: </strong></font><a href="http://sauvy.ined.fr/brouard"><font  <p align="center"><font color="#00006A"><strong>Authors of the
 color="#00006A"><strong>Nicolas Brouard</strong></font></a><font  program: </strong></font><a href="http://sauvy.ined.fr/brouard"><font
 color="#00006A"><strong>, senior researcher at the </strong></font><a  color="#00006A"><strong>Nicolas Brouard</strong></font></a><font
 href="http://www.ined.fr"><font color="#00006A"><strong>Institut  color="#00006A"><strong>, senior researcher at the </strong></font><a
 National d'Etudes Démographiques</strong></font></a><font  href="http://www.ined.fr"><font color="#00006A"><strong>Institut
 color="#00006A"><strong> (INED, Paris) in the &quot;Mortality,  National d'Etudes Démographiques</strong></font></a><font
 Health and Epidemiology&quot; Research Unit </strong></font></p>  color="#00006A"><strong> (INED, Paris) in the &quot;Mortality,
   Health and Epidemiology&quot; Research Unit </strong></font></p>
 <p align="center"><font color="#00006A"><strong>and Agnès  
 Lièvre<br clear="left">  <p align="center"><font color="#00006A"><strong>and Agnès
 </strong></font></p>  Lièvre<br clear="left">
   </strong></font></p>
 <h4><font color="#00006A">Contribution to the mathematics: C. R.  
 Heathcote </font><font color="#00006A" size="2">(Australian  <h4><font color="#00006A">Contribution to the mathematics: C. R.
 National University, Canberra).</font></h4>  Heathcote </font><font color="#00006A" size="2">(Australian
   National University, Canberra).</font></h4>
 <h4><font color="#00006A">Contact: Agnès Lièvre (</font><a  
 href="mailto:lievre@ined.fr"><font color="#00006A"><i>lievre@ined.fr</i></font></a><font  <h4><font color="#00006A">Contact: Agnès Lièvre (</font><a
 color="#00006A">) </font></h4>  href="mailto:lievre@ined.fr"><font color="#00006A"><i>lievre@ined.fr</i></font></a><font
   color="#00006A">) </font></h4>
 <hr>  
   <hr>
 <ul>  
     <li><a href="#intro">Introduction</a> </li>  <ul>
     <li>The detailed statistical model (<a href="docmath.pdf">PDF      <li><a href="#intro">Introduction</a> </li>
         version</a>),(<a href="docmath.ps">ps version</a>) </li>      <li>The detailed statistical model (<a href="docmath.pdf">PDF
     <li><a href="#data">On what kind of data can it be used?</a></li>          version</a>),(<a href="docmath.ps">ps version</a>) </li>
     <li><a href="#datafile">The data file</a> </li>      <li><a href="#data">On what kind of data can it be used?</a></li>
     <li><a href="#biaspar">The parameter file</a> </li>      <li><a href="#datafile">The data file</a> </li>
     <li><a href="#running">Running Imach</a> </li>      <li><a href="#biaspar">The parameter file</a> </li>
     <li><a href="#output">Output files and graphs</a> </li>      <li><a href="#running">Running Imach</a> </li>
     <li><a href="#example">Exemple</a> </li>      <li><a href="#output">Output files and graphs</a> </li>
 </ul>      <li><a href="#example">Exemple</a> </li>
   </ul>
 <hr>  
   <hr>
 <h2><a name="intro"><font color="#00006A">Introduction</font></a></h2>  
   <h2><a name="intro"><font color="#00006A">Introduction</font></a></h2>
 <p>This program computes <b>Healthy Life Expectancies</b> from <b>cross-longitudinal  
 data</b>. Within the family of Health Expectancies (HE),  <p>This program computes <b>Healthy Life Expectancies</b> from <b>cross-longitudinal
 Disability-free life expectancy (DFLE) is probably the most  data</b> using the methodology pioneered by Laditka and Wolf (1).
 important index to monitor. In low mortality countries, there is  Within the family of Health Expectancies (HE), Disability-free
 a fear that when mortality declines, the increase in DFLE is not  life expectancy (DFLE) is probably the most important index to
 proportionate to the increase in total Life expectancy. This case  monitor. In low mortality countries, there is a fear that when
 is called the <em>Expansion of morbidity</em>. Most of the data  mortality declines, the increase in DFLE is not proportionate to
 collected today, in particular by the international <a  the increase in total Life expectancy. This case is called the <em>Expansion
 href="http://euroreves/reves">REVES</a> network on Health  of morbidity</em>. Most of the data collected today, in
 expectancy, and most HE indices based on these data, are <em>cross-sectional</em>.  particular by the international <a href="http://euroreves/reves">REVES</a>
 It means that the information collected comes from a single  network on Health expectancy, and most HE indices based on these
 cross-sectional survey: people from various ages (but mostly old  data, are <em>cross-sectional</em>. It means that the information
 people) are surveyed on their health status at a single date.  collected comes from a single cross-sectional survey: people from
 Proportion of people disabled at each age, can then be measured  various ages (but mostly old people) are surveyed on their health
 at that date. This age-specific prevalence curve is then used to  status at a single date. Proportion of people disabled at each
 distinguish, within the stationary population (which, by  age, can then be measured at that date. This age-specific
 definition, is the life table estimated from the vital statistics  prevalence curve is then used to distinguish, within the
 on mortality at the same date), the disable population from the  stationary population (which, by definition, is the life table
 disability-free population. Life expectancy (LE) (or total  estimated from the vital statistics on mortality at the same
 population divided by the yearly number of births or deaths of  date), the disable population from the disability-free
 this stationary population) is then decomposed into DFLE and DLE.  population. Life expectancy (LE) (or total population divided by
 This method of computing HE is usually called the Sullivan method  the yearly number of births or deaths of this stationary
 (from the name of the author who first described it).</p>  population) is then decomposed into DFLE and DLE. This method of
   computing HE is usually called the Sullivan method (from the name
 <p>Age-specific proportions of people disable are very difficult  of the author who first described it).</p>
 to forecast because each proportion corresponds to historical  
 conditions of the cohort and it is the result of the historical  <p>Age-specific proportions of people disable are very difficult
 flows from entering disability and recovering in the past until  to forecast because each proportion corresponds to historical
 today. The age-specific intensities (or incidence rates) of  conditions of the cohort and it is the result of the historical
 entering disability or recovering a good health, are reflecting  flows from entering disability and recovering in the past until
 actual conditions and therefore can be used at each age to  today. The age-specific intensities (or incidence rates) of
 forecast the future of this cohort. For example if a country is  entering disability or recovering a good health, are reflecting
 improving its technology of prosthesis, the incidence of  actual conditions and therefore can be used at each age to
 recovering the ability to walk will be higher at each (old) age,  forecast the future of this cohort. For example if a country is
 but the prevalence of disability will only slightly reflect an  improving its technology of prosthesis, the incidence of
 improve because the prevalence is mostly affected by the history  recovering the ability to walk will be higher at each (old) age,
 of the cohort and not by recent period effects. To measure the  but the prevalence of disability will only slightly reflect an
 period improvement we have to simulate the future of a cohort of  improve because the prevalence is mostly affected by the history
 new-borns entering or leaving at each age the disability state or  of the cohort and not by recent period effects. To measure the
 dying according to the incidence rates measured today on  period improvement we have to simulate the future of a cohort of
 different cohorts. The proportion of people disabled at each age  new-borns entering or leaving at each age the disability state or
 in this simulated cohort will be much lower (using the exemple of  dying according to the incidence rates measured today on
 an improvement) that the proportions observed at each age in a  different cohorts. The proportion of people disabled at each age
 cross-sectional survey. This new prevalence curve introduced in a  in this simulated cohort will be much lower (using the exemple of
 life table will give a much more actual and realistic HE level  an improvement) that the proportions observed at each age in a
 than the Sullivan method which mostly measured the History of  cross-sectional survey. This new prevalence curve introduced in a
 health conditions in this country.</p>  life table will give a much more actual and realistic HE level
   than the Sullivan method which mostly measured the History of
 <p>Therefore, the main question is how to measure incidence rates  health conditions in this country.</p>
 from cross-longitudinal surveys? This is the goal of the IMaCH  
 program. From your data and using IMaCH you can estimate period  <p>Therefore, the main question is how to measure incidence rates
 HE and not only Sullivan's HE. Also the standard errors of the HE  from cross-longitudinal surveys? This is the goal of the IMaCH
 are computed.</p>  program. From your data and using IMaCH you can estimate period
   HE and not only Sullivan's HE. Also the standard errors of the HE
 <p>A cross-longitudinal survey consists in a first survey  are computed.</p>
 (&quot;cross&quot;) where individuals from different ages are  
 interviewed on their health status or degree of disability. At  <p>A cross-longitudinal survey consists in a first survey
 least a second wave of interviews (&quot;longitudinal&quot;)  (&quot;cross&quot;) where individuals from different ages are
 should measure each new individual health status. Health  interviewed on their health status or degree of disability. At
 expectancies are computed from the transitions observed between  least a second wave of interviews (&quot;longitudinal&quot;)
 waves and are computed for each degree of severity of disability  should measure each new individual health status. Health
 (number of life states). More degrees you consider, more time is  expectancies are computed from the transitions observed between
 necessary to reach the Maximum Likelihood of the parameters  waves and are computed for each degree of severity of disability
 involved in the model. Considering only two states of disability  (number of life states). More degrees you consider, more time is
 (disable and healthy) is generally enough but the computer  necessary to reach the Maximum Likelihood of the parameters
 program works also with more health statuses.<br>  involved in the model. Considering only two states of disability
 <br>  (disable and healthy) is generally enough but the computer
 The simplest model is the multinomial logistic model where <i>pij</i>  program works also with more health statuses.<br>
 is the probability to be observed in state <i>j</i> at the second  <br>
 wave conditional to be observed in state <em>i</em> at the first  The simplest model is the multinomial logistic model where <i>pij</i>
 wave. Therefore a simple model is: log<em>(pij/pii)= aij +  is the probability to be observed in state <i>j</i> at the second
 bij*age+ cij*sex,</em> where '<i>age</i>' is age and '<i>sex</i>'  wave conditional to be observed in state <em>i</em> at the first
 is a covariate. The advantage that this computer program claims,  wave. Therefore a simple model is: log<em>(pij/pii)= aij +
 comes from that if the delay between waves is not identical for  bij*age+ cij*sex,</em> where '<i>age</i>' is age and '<i>sex</i>'
 each individual, or if some individual missed an interview, the  is a covariate. The advantage that this computer program claims,
 information is not rounded or lost, but taken into account using  comes from that if the delay between waves is not identical for
 an interpolation or extrapolation. <i>hPijx</i> is the  each individual, or if some individual missed an interview, the
 probability to be observed in state <i>i</i> at age <i>x+h</i>  information is not rounded or lost, but taken into account using
 conditional to the observed state <i>i</i> at age <i>x</i>. The  an interpolation or extrapolation. <i>hPijx</i> is the
 delay '<i>h</i>' can be split into an exact number (<i>nh*stepm</i>)  probability to be observed in state <i>i</i> at age <i>x+h</i>
 of unobserved intermediate states. This elementary transition (by  conditional to the observed state <i>i</i> at age <i>x</i>. The
 month or quarter trimester, semester or year) is modeled as a  delay '<i>h</i>' can be split into an exact number (<i>nh*stepm</i>)
 multinomial logistic. The <i>hPx</i> matrix is simply the matrix  of unobserved intermediate states. This elementary transition (by
 product of <i>nh*stepm</i> elementary matrices and the  month or quarter trimester, semester or year) is modeled as a
 contribution of each individual to the likelihood is simply <i>hPijx</i>.  multinomial logistic. The <i>hPx</i> matrix is simply the matrix
 <br>  product of <i>nh*stepm</i> elementary matrices and the
 </p>  contribution of each individual to the likelihood is simply <i>hPijx</i>.
   <br>
 <p>The program presented in this manual is a quite general  </p>
 program named <strong>IMaCh</strong> (for <strong>I</strong>nterpolated  
 <strong>MA</strong>rkov <strong>CH</strong>ain), designed to  <p>The program presented in this manual is a quite general
 analyse transition data from longitudinal surveys. The first step  program named <strong>IMaCh</strong> (for <strong>I</strong>nterpolated
 is the parameters estimation of a transition probabilities model  <strong>MA</strong>rkov <strong>CH</strong>ain), designed to
 between an initial status and a final status. From there, the  analyse transition data from longitudinal surveys. The first step
 computer program produces some indicators such as observed and  is the parameters estimation of a transition probabilities model
 stationary prevalence, life expectancies and their variances and  between an initial status and a final status. From there, the
 graphs. Our transition model consists in absorbing and  computer program produces some indicators such as observed and
 non-absorbing states with the possibility of return across the  stationary prevalence, life expectancies and their variances and
 non-absorbing states. The main advantage of this package,  graphs. Our transition model consists in absorbing and
 compared to other programs for the analysis of transition data  non-absorbing states with the possibility of return across the
 (For example: Proc Catmod of SAS<sup>®</sup>) is that the whole  non-absorbing states. The main advantage of this package,
 individual information is used even if an interview is missing, a  compared to other programs for the analysis of transition data
 status or a date is unknown or when the delay between waves is  (For example: Proc Catmod of SAS<sup>®</sup>) is that the whole
 not identical for each individual. The program can be executed  individual information is used even if an interview is missing, a
 according to parameters: selection of a sub-sample, number of  status or a date is unknown or when the delay between waves is
 absorbing and non-absorbing states, number of waves taken in  not identical for each individual. The program can be executed
 account (the user inputs the first and the last interview), a  according to parameters: selection of a sub-sample, number of
 tolerance level for the maximization function, the periodicity of  absorbing and non-absorbing states, number of waves taken in
 the transitions (we can compute annual, quaterly or monthly  account (the user inputs the first and the last interview), a
 transitions), covariates in the model. It works on Windows or on  tolerance level for the maximization function, the periodicity of
 Unix.<br>  the transitions (we can compute annual, quaterly or monthly
 </p>  transitions), covariates in the model. It works on Windows or on
   Unix.<br>
 <hr>  </p>
   
 <h2><a name="data"><font color="#00006A">On what kind of data can  <hr>
 it be used?</font></a></h2>  
   <p>(1) Laditka, Sarah B. and Wolf, Douglas A. (1998), &quot;New
 <p>The minimum data required for a transition model is the  Methods for Analyzing Active Life Expectancy&quot;. <i>Journal of
 recording of a set of individuals interviewed at a first date and  Aging and Health</i>. Vol 10, No. 2. </p>
 interviewed again at least one another time. From the  
 observations of an individual, we obtain a follow-up over time of  <hr>
 the occurrence of a specific event. In this documentation, the  
 event is related to health status at older ages, but the program  <h2><a name="data"><font color="#00006A">On what kind of data can
 can be applied on a lot of longitudinal studies in different  it be used?</font></a></h2>
 contexts. To build the data file explained into the next section,  
 you must have the month and year of each interview and the  <p>The minimum data required for a transition model is the
 corresponding health status. But in order to get age, date of  recording of a set of individuals interviewed at a first date and
 birth (month and year) is required (missing values is allowed for  interviewed again at least one another time. From the
 month). Date of death (month and year) is an important  observations of an individual, we obtain a follow-up over time of
 information also required if the individual is dead. Shorter  the occurrence of a specific event. In this documentation, the
 steps (i.e. a month) will more closely take into account the  event is related to health status at older ages, but the program
 survival time after the last interview.</p>  can be applied on a lot of longitudinal studies in different
   contexts. To build the data file explained into the next section,
 <hr>  you must have the month and year of each interview and the
   corresponding health status. But in order to get age, date of
 <h2><a name="datafile"><font color="#00006A">The data file</font></a></h2>  birth (month and year) is required (missing values is allowed for
   month). Date of death (month and year) is an important
 <p>In this example, 8,000 people have been interviewed in a  information also required if the individual is dead. Shorter
 cross-longitudinal survey of 4 waves (1984, 1986, 1988, 1990).  steps (i.e. a month) will more closely take into account the
 Some people missed 1, 2 or 3 interviews. Health statuses are  survival time after the last interview.</p>
 healthy (1) and disable (2). The survey is not a real one. It is  
 a simulation of the American Longitudinal Survey on Aging. The  <hr>
 disability state is defined if the individual missed one of four  
 ADL (Activity of daily living, like bathing, eating, walking).  <h2><a name="datafile"><font color="#00006A">The data file</font></a></h2>
 Therefore, even is the individuals interviewed in the sample are  
 virtual, the information brought with this sample is close to the  <p>In this example, 8,000 people have been interviewed in a
 situation of the United States. Sex is not recorded is this  cross-longitudinal survey of 4 waves (1984, 1986, 1988, 1990).
 sample.</p>  Some people missed 1, 2 or 3 interviews. Health statuses are
   healthy (1) and disable (2). The survey is not a real one. It is
 <p>Each line of the data set (named <a href="data1.txt">data1.txt</a>  a simulation of the American Longitudinal Survey on Aging. The
 in this first example) is an individual record which fields are: </p>  disability state is defined if the individual missed one of four
   ADL (Activity of daily living, like bathing, eating, walking).
 <ul>  Therefore, even is the individuals interviewed in the sample are
     <li><b>Index number</b>: positive number (field 1) </li>  virtual, the information brought with this sample is close to the
     <li><b>First covariate</b> positive number (field 2) </li>  situation of the United States. Sex is not recorded is this
     <li><b>Second covariate</b> positive number (field 3) </li>  sample.</p>
     <li><a name="Weight"><b>Weight</b></a>: positive number  
         (field 4) . In most surveys individuals are weighted  <p>Each line of the data set (named <a href="data1.txt">data1.txt</a>
         according to the stratification of the sample.</li>  in this first example) is an individual record which fields are: </p>
     <li><b>Date of birth</b>: coded as mm/yyyy. Missing dates are  
         coded as 99/9999 (field 5) </li>  <ul>
     <li><b>Date of death</b>: coded as mm/yyyy. Missing dates are      <li><b>Index number</b>: positive number (field 1) </li>
         coded as 99/9999 (field 6) </li>      <li><b>First covariate</b> positive number (field 2) </li>
     <li><b>Date of first interview</b>: coded as mm/yyyy. Missing      <li><b>Second covariate</b> positive number (field 3) </li>
         dates are coded as 99/9999 (field 7) </li>      <li><a name="Weight"><b>Weight</b></a>: positive number
     <li><b>Status at first interview</b>: positive number.          (field 4) . In most surveys individuals are weighted
         Missing values ar coded -1. (field 8) </li>          according to the stratification of the sample.</li>
     <li><b>Date of second interview</b>: coded as mm/yyyy.      <li><b>Date of birth</b>: coded as mm/yyyy. Missing dates are
         Missing dates are coded as 99/9999 (field 9) </li>          coded as 99/9999 (field 5) </li>
     <li><strong>Status at second interview</strong> positive      <li><b>Date of death</b>: coded as mm/yyyy. Missing dates are
         number. Missing values ar coded -1. (field 10) </li>          coded as 99/9999 (field 6) </li>
     <li><b>Date of third interview</b>: coded as mm/yyyy. Missing      <li><b>Date of first interview</b>: coded as mm/yyyy. Missing
         dates are coded as 99/9999 (field 11) </li>          dates are coded as 99/9999 (field 7) </li>
     <li><strong>Status at third interview</strong> positive      <li><b>Status at first interview</b>: positive number.
         number. Missing values ar coded -1. (field 12) </li>          Missing values ar coded -1. (field 8) </li>
     <li><b>Date of fourth interview</b>: coded as mm/yyyy.      <li><b>Date of second interview</b>: coded as mm/yyyy.
         Missing dates are coded as 99/9999 (field 13) </li>          Missing dates are coded as 99/9999 (field 9) </li>
     <li><strong>Status at fourth interview</strong> positive      <li><strong>Status at second interview</strong> positive
         number. Missing values are coded -1. (field 14) </li>          number. Missing values ar coded -1. (field 10) </li>
     <li>etc</li>      <li><b>Date of third interview</b>: coded as mm/yyyy. Missing
 </ul>          dates are coded as 99/9999 (field 11) </li>
       <li><strong>Status at third interview</strong> positive
 <p>&nbsp;</p>          number. Missing values ar coded -1. (field 12) </li>
       <li><b>Date of fourth interview</b>: coded as mm/yyyy.
 <p>If your longitudinal survey do not include information about          Missing dates are coded as 99/9999 (field 13) </li>
 weights or covariates, you must fill the column with a number      <li><strong>Status at fourth interview</strong> positive
 (e.g. 1) because a missing field is not allowed.</p>          number. Missing values are coded -1. (field 14) </li>
       <li>etc</li>
 <hr>  </ul>
   
 <h2><font color="#00006A">Your first example parameter file</font><a  <p>&nbsp;</p>
 href="http://euroreves.ined.fr/imach"></a><a name="uio"></a></h2>  
   <p>If your longitudinal survey do not include information about
 <h2><a name="biaspar"></a>#Imach version 0.63, February 2000,  weights or covariates, you must fill the column with a number
 INED-EUROREVES </h2>  (e.g. 1) because a missing field is not allowed.</p>
   
 <p>This is a comment. Comments start with a '#'.</p>  <hr>
   
 <h4><font color="#FF0000">First uncommented line</font></h4>  <h2><font color="#00006A">Your first example parameter file</font><a
   href="http://euroreves.ined.fr/imach"></a><a name="uio"></a></h2>
 <pre>title=1st_example datafile=data1.txt lastobs=8600 firstpass=1 lastpass=4</pre>  
   <h2><a name="biaspar"></a>#Imach version 0.63, February 2000,
 <ul>  INED-EUROREVES </h2>
     <li><b>title=</b> 1st_example is title of the run. </li>  
     <li><b>datafile=</b>data1.txt is the name of the data set.  <p>This is a comment. Comments start with a '#'.</p>
         Our example is a six years follow-up survey. It consists  
         in a baseline followed by 3 reinterviews. </li>  <h4><font color="#FF0000">First uncommented line</font></h4>
     <li><b>lastobs=</b> 8600 the program is able to run on a  
         subsample where the last observation number is lastobs.  <pre>title=1st_example datafile=data1.txt lastobs=8600 firstpass=1 lastpass=4</pre>
         It can be set a bigger number than the real number of  
         observations (e.g. 100000). In this example, maximisation  <ul>
         will be done on the 8600 first records. </li>      <li><b>title=</b> 1st_example is title of the run. </li>
     <li><b>firstpass=1</b> , <b>lastpass=4 </b>In case of more      <li><b>datafile=</b>data1.txt is the name of the data set.
         than two interviews in the survey, the program can be run          Our example is a six years follow-up survey. It consists
         on selected transitions periods. firstpass=1 means the          in a baseline followed by 3 reinterviews. </li>
         first interview included in the calculation is the      <li><b>lastobs=</b> 8600 the program is able to run on a
         baseline survey. lastpass=4 means that the information          subsample where the last observation number is lastobs.
         brought by the 4th interview is taken into account.</li>          It can be set a bigger number than the real number of
 </ul>          observations (e.g. 100000). In this example, maximisation
           will be done on the 8600 first records. </li>
 <p>&nbsp;</p>      <li><b>firstpass=1</b> , <b>lastpass=4 </b>In case of more
           than two interviews in the survey, the program can be run
 <h4><a name="biaspar-2"><font color="#FF0000">Second uncommented          on selected transitions periods. firstpass=1 means the
 line</font></a></h4>          first interview included in the calculation is the
           baseline survey. lastpass=4 means that the information
 <pre>ftol=1.e-08 stepm=1 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0</pre>          brought by the 4th interview is taken into account.</li>
   </ul>
 <ul>  
     <li><b>ftol=1e-8</b> Convergence tolerance on the function  <p>&nbsp;</p>
         value in the maximisation of the likelihood. Choosing a  
         correct value for ftol is difficult. 1e-8 is a correct  <h4><a name="biaspar-2"><font color="#FF0000">Second uncommented
         value for a 32 bits computer.</li>  line</font></a></h4>
     <li><b>stepm=1</b> Time unit in months for interpolation.  
         Examples:<ul>  <pre>ftol=1.e-08 stepm=1 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0</pre>
             <li>If stepm=1, the unit is a month </li>  
             <li>If stepm=4, the unit is a trimester</li>  <ul>
             <li>If stepm=12, the unit is a year </li>      <li><b>ftol=1e-8</b> Convergence tolerance on the function
             <li>If stepm=24, the unit is two years</li>          value in the maximisation of the likelihood. Choosing a
             <li>... </li>          correct value for ftol is difficult. 1e-8 is a correct
         </ul>          value for a 32 bits computer.</li>
     </li>      <li><b>stepm=1</b> Time unit in months for interpolation.
     <li><b>ncov=2</b> Number of covariates to be add to the          Examples:<ul>
         model. The intercept and the age parameter are counting              <li>If stepm=1, the unit is a month </li>
         for 2 covariates. For example, if you want to add gender              <li>If stepm=4, the unit is a trimester</li>
         in the covariate vector you must write ncov=3 else              <li>If stepm=12, the unit is a year </li>
         ncov=2. </li>              <li>If stepm=24, the unit is two years</li>
     <li><b>nlstate=2</b> Number of non-absorbing (live) states.              <li>... </li>
         Here we have two alive states: disability-free is coded 1          </ul>
         and disability is coded 2. </li>      </li>
     <li><b>ndeath=1</b> Number of absorbing states. The absorbing      <li><b>ncov=2</b> Number of covariates in the datafile. The
         state death is coded 3. </li>          intercept and the age parameter are counting for 2
     <li><b>maxwav=4</b> Maximum number of waves. The program can          covariates.</li>
         not include more than 4 interviews. </li>      <li><b>nlstate=2</b> Number of non-absorbing (alive) states.
     <li><a name="mle"><b>mle</b></a><b>=1</b> Option for the          Here we have two alive states: disability-free is coded 1
         Maximisation Likelihood Estimation. <ul>          and disability is coded 2. </li>
             <li>If mle=1 the program does the maximisation and      <li><b>ndeath=1</b> Number of absorbing states. The absorbing
                 the calculation of heath expectancies </li>          state death is coded 3. </li>
             <li>If mle=0 the program only does the calculation of      <li><b>maxwav=4</b> Number of waves in the datafile.</li>
                 the health expectancies. </li>      <li><a name="mle"><b>mle</b></a><b>=1</b> Option for the
         </ul>          Maximisation Likelihood Estimation. <ul>
     </li>              <li>If mle=1 the program does the maximisation and
     <li><b>weight=0</b> Possibility to add weights. <ul>                  the calculation of health expectancies </li>
             <li>If weight=0 no weights are included </li>              <li>If mle=0 the program only does the calculation of
             <li>If weight=1 the maximisation integrates the                  the health expectancies. </li>
                 weights which are in field <a href="#Weight">4</a></li>          </ul>
         </ul>      </li>
     </li>      <li><b>weight=0</b> Possibility to add weights. <ul>
 </ul>              <li>If weight=0 no weights are included </li>
               <li>If weight=1 the maximisation integrates the
 <h4><font color="#FF0000">Guess values for optimization</font><font                  weights which are in field <a href="#Weight">4</a></li>
 color="#00006A"> </font></h4>          </ul>
       </li>
 <p>You must write the initial guess values of the parameters for  </ul>
 optimization. The number of parameters, <em>N</em> depends on the  
 number of absorbing states and non-absorbing states and on the  <h4><font color="#FF0000">Covariates</font></h4>
 number of covariates. <br>  
 <em>N</em> is given by the formula <em>N</em>=(<em>nlstate</em> +  <p>Intercept and age are systematically included in the model.
 <em>ndeath</em>-1)*<em>nlstate</em>*<em>ncov</em>&nbsp;. <br>  Additional covariates can be included with the command </p>
 <br>  
 Thus in the simple case with 2 covariates (the model is log  <pre>model=<em>list of covariates</em></pre>
 (pij/pii) = aij + bij * age where intercept and age are the two  
 covariates), and 2 health degrees (1 for disability-free and 2  <ul>
 for disability) and 1 absorbing state (3), you must enter 8      <li>if<strong> model=. </strong>then no covariates are
 initials values, a12, b12, a13, b13, a21, b21, a23, b23. You can          included</li>
 start with zeros as in this example, but if you have a more      <li>if <strong>model=V1</strong> the model includes the first
 precise set (for example from an earlier run) you can enter it          covariate (field 2)</li>
 and it will speed up them<br>      <li>if <strong>model=V2 </strong>the model includes the
 Each of the four lines starts with indices &quot;ij&quot;: <br>          second covariate (field 3)</li>
 <br>      <li>if <strong>model=V1+V2 </strong>the model includes the
 <b>ij aij bij</b> </p>          first and the second covariate (fields 2 and 3)</li>
       <li>if <strong>model=V1*V2 </strong>the model includes the
 <blockquote>          product of the first and the second covariate (fields 2
     <pre># Guess values of aij and bij in log (pij/pii) = aij + bij * age          and 3)</li>
 12 -14.155633  0.110794   </ul>
 13  -7.925360  0.032091   
 21  -1.890135 -0.029473   <h4><font color="#FF0000">Guess values for optimization</font><font
 23  -6.234642  0.022315 </pre>  color="#00006A"> </font></h4>
 </blockquote>  
   <p>You must write the initial guess values of the parameters for
 <p>or, to simplify: </p>  optimization. The number of parameters, <em>N</em> depends on the
   number of absorbing states and non-absorbing states and on the
 <blockquote>  number of covariates. <br>
     <pre>12 0.0 0.0  <em>N</em> is given by the formula <em>N</em>=(<em>nlstate</em> +
 13 0.0 0.0  <em>ndeath</em>-1)*<em>nlstate</em>*<em>ncov</em>&nbsp;. <br>
 21 0.0 0.0  <br>
 23 0.0 0.0</pre>  Thus in the simple case with 2 covariates (the model is log
 </blockquote>  (pij/pii) = aij + bij * age where intercept and age are the two
   covariates), and 2 health degrees (1 for disability-free and 2
 <h4><font color="#FF0000">Guess values for computing variances</font></h4>  for disability) and 1 absorbing state (3), you must enter 8
   initials values, a12, b12, a13, b13, a21, b21, a23, b23. You can
 <p>This is an output if <a href="#mle">mle</a>=1. But it can be  start with zeros as in this example, but if you have a more
 used as an input to get the vairous output data files (Health  precise set (for example from an earlier run) you can enter it
 expectancies, stationary prevalence etc.) and figures without  and it will speed up them<br>
 rerunning the rather long maximisation phase (mle=0). </p>  Each of the four lines starts with indices &quot;ij&quot;: <br>
   <br>
 <p>The scales are small values for the evaluation of numerical  <b>ij aij bij</b> </p>
 derivatives. These derivatives are used to compute the hessian  
 matrix of the parameters, that is the inverse of the covariance  <blockquote>
 matrix, and the variances of health expectancies. Each line      <pre># Guess values of aij and bij in log (pij/pii) = aij + bij * age
 consists in indices &quot;ij&quot; followed by the initial scales  12 -14.155633  0.110794
 (zero to simplify) associated with aij and bij. </p>  13  -7.925360  0.032091
   21  -1.890135 -0.029473
 <ul>  23  -6.234642  0.022315 </pre>
     <li>If mle=1 you can enter zeros:</li>  </blockquote>
 </ul>  
   <p>or, to simplify: </p>
 <blockquote>  
     <pre># Scales (for hessian or gradient estimation)  <blockquote>
 12 0. 0.       <pre>12 0.0 0.0
 13 0. 0.   13 0.0 0.0
 21 0. 0.   21 0.0 0.0
 23 0. 0. </pre>  23 0.0 0.0</pre>
 </blockquote>  </blockquote>
   
 <ul>  <h4><font color="#FF0000">Guess values for computing variances</font></h4>
     <li>If mle=0 you must enter a covariance matrix (usually  
         obtained from an earlier run).</li>  <p>This is an output if <a href="#mle">mle</a>=1. But it can be
 </ul>  used as an input to get the vairous output data files (Health
   expectancies, stationary prevalence etc.) and figures without
 <h4><font color="#FF0000">Covariance matrix of parameters</font></h4>  rerunning the rather long maximisation phase (mle=0). </p>
   
 <p>This is an output if <a href="#mle">mle</a>=1. But it can be  <p>The scales are small values for the evaluation of numerical
 used as an input to get the vairous output data files (Health  derivatives. These derivatives are used to compute the hessian
 expectancies, stationary prevalence etc.) and figures without  matrix of the parameters, that is the inverse of the covariance
 rerunning the rather long maximisation phase (mle=0). </p>  matrix, and the variances of health expectancies. Each line
   consists in indices &quot;ij&quot; followed by the initial scales
 <p>Each line starts with indices &quot;ijk&quot; followed by the  (zero to simplify) associated with aij and bij. </p>
 covariances between aij and bij: </p>  
   <ul>
 <pre>      <li>If mle=1 you can enter zeros:</li>
    121 Var(a12)   </ul>
    122 Cov(b12,a12)  Var(b12)   
           ...  <blockquote>
    232 Cov(b23,a12)  Cov(b23,b12) ... Var (b23) </pre>      <pre># Scales (for hessian or gradient estimation)
   12 0. 0.
 <ul>  13 0. 0.
     <li>If mle=1 you can enter zeros. </li>  21 0. 0.
 </ul>  23 0. 0. </pre>
   </blockquote>
 <blockquote>  
     <pre># Covariance matrix  <ul>
 121 0.      <li>If mle=0 you must enter a covariance matrix (usually
 122 0. 0.          obtained from an earlier run).</li>
 131 0. 0. 0.   </ul>
 132 0. 0. 0. 0.   
 211 0. 0. 0. 0. 0.   <h4><font color="#FF0000">Covariance matrix of parameters</font></h4>
 212 0. 0. 0. 0. 0. 0.   
 231 0. 0. 0. 0. 0. 0. 0.   <p>This is an output if <a href="#mle">mle</a>=1. But it can be
 232 0. 0. 0. 0. 0. 0. 0. 0.</pre>  used as an input to get the vairous output data files (Health
 </blockquote>  expectancies, stationary prevalence etc.) and figures without
   rerunning the rather long maximisation phase (mle=0). </p>
 <ul>  
     <li>If mle=0 you must enter a covariance matrix (usually  <p>Each line starts with indices &quot;ijk&quot; followed by the
         obtained from an earlier run).<br>  covariances between aij and bij: </p>
         </li>  
 </ul>  <pre>
      121 Var(a12)
 <h4><a name="biaspar-l"></a><font color="#FF0000">last     122 Cov(b12,a12)  Var(b12)
 uncommented line</font></h4>            ...
      232 Cov(b23,a12)  Cov(b23,b12) ... Var (b23) </pre>
 <pre>agemin=70 agemax=100 bage=50 fage=100</pre>  
   <ul>
 <p>Once we obtained the estimated parameters, the program is able      <li>If mle=1 you can enter zeros. </li>
 to calculated stationary prevalence, transitions probabilities  </ul>
 and life expectancies at any age. Choice of age ranges is useful  
 for extrapolation. In our data file, ages varies from age 70 to  <blockquote>
 102. Setting bage=50 and fage=100, makes the program computing      <pre># Covariance matrix
 life expectancy from age bage to age fage. As we use a model, we  121 0.
 can compute life expectancy on a wider age range than the age  122 0. 0.
 range from the data. But the model can be rather wrong on big  131 0. 0. 0.
 intervals.</p>  132 0. 0. 0. 0.
   211 0. 0. 0. 0. 0.
 <p>Similarly, it is possible to get extrapolated stationary  212 0. 0. 0. 0. 0. 0.
 prevalence by age raning from agemin to agemax. </p>  231 0. 0. 0. 0. 0. 0. 0.
   232 0. 0. 0. 0. 0. 0. 0. 0.</pre>
 <ul>  </blockquote>
     <li><b>agemin=</b> Minimum age for calculation of the  
         stationary prevalence </li>  <ul>
     <li><b>agemax=</b> Maximum age for calculation of the      <li>If mle=0 you must enter a covariance matrix (usually
         stationary prevalence </li>          obtained from an earlier run).<br>
     <li><b>bage=</b> Minimum age for calculation of the health          </li>
         expectancies </li>  </ul>
     <li><b>fage=</b> Maximum ages for calculation of the health  
         expectancies </li>  <h4><a name="biaspar-l"></a><font color="#FF0000">last
 </ul>  uncommented line</font></h4>
   
 <hr>  <pre>agemin=70 agemax=100 bage=50 fage=100</pre>
   
 <h2><a name="running"></a><font color="#00006A">Running Imach  <p>Once we obtained the estimated parameters, the program is able
 with this example</font></h2>  to calculated stationary prevalence, transitions probabilities
   and life expectancies at any age. Choice of age ranges is useful
 <p>We assume that you entered your <a href="biaspar.txt">1st_example  for extrapolation. In our data file, ages varies from age 70 to
 parameter file</a> as explained <a href="#biaspar">above</a>. To  102. Setting bage=50 and fage=100, makes the program computing
 run the program you should click on the imach.exe icon and enter  life expectancy from age bage to age fage. As we use a model, we
 the name of the parameter file which is for example <a  can compute life expectancy on a wider age range than the age
 href="C:\usr\imach\mle\biaspar.txt">C:\usr\imach\mle\biaspar.txt</a>  range from the data. But the model can be rather wrong on big
 (you also can click on the biaspar.txt icon located in <br>  intervals.</p>
 <a href="C:\usr\imach\mle">C:\usr\imach\mle</a> and put it with  
 the mouse on the imach window).<br>  <p>Similarly, it is possible to get extrapolated stationary
 </p>  prevalence by age raning from agemin to agemax. </p>
   
 <p>The time to converge depends on the step unit that you used (1  <ul>
 month is cpu consuming), on the number of cases, and on the      <li><b>agemin=</b> Minimum age for calculation of the
 number of variables.</p>          stationary prevalence </li>
       <li><b>agemax=</b> Maximum age for calculation of the
 <p>The program outputs many files. Most of them are files which          stationary prevalence </li>
 will be plotted for better understanding.</p>      <li><b>bage=</b> Minimum age for calculation of the health
           expectancies </li>
 <hr>      <li><b>fage=</b> Maximum ages for calculation of the health
           expectancies </li>
 <h2><a name="output"><font color="#00006A">Output of the program  </ul>
 and graphs</font> </a></h2>  
   <hr>
 <p>Once the optimization is finished, some graphics can be made  
 with a grapher. We use Gnuplot which is an interactive plotting  <h2><a name="running"></a><font color="#00006A">Running Imach
 program copyrighted but freely distributed. Imach outputs the  with this example</font></h2>
 source of a gnuplot file, named 'graph.gp', which can be directly  
 input into gnuplot.<br>  <p>We assume that you entered your <a href="biaspar.txt">1st_example
 When the running is finished, the user should enter a caracter  parameter file</a> as explained <a href="#biaspar">above</a>. To
 for plotting and output editing. </p>  run the program you should click on the imach.exe icon and enter
   the name of the parameter file which is for example <a
 <p>These caracters are:</p>  href="C:\usr\imach\mle\biaspar.txt">C:\usr\imach\mle\biaspar.txt</a>
   (you also can click on the biaspar.txt icon located in <br>
 <ul>  <a href="C:\usr\imach\mle">C:\usr\imach\mle</a> and put it with
     <li>'c' to start again the program from the beginning.</li>  the mouse on the imach window).<br>
     <li>'g' to made graphics. The output graphs are in GIF format  </p>
         and you have no control over which is produced. If you  
         want to modify the graphics or make another one, you  <p>The time to converge depends on the step unit that you used (1
         should modify the parameters in the file <b>graph.gp</b>  month is cpu consuming), on the number of cases, and on the
         located in imach\bin. A gnuplot reference manual is  number of variables.</p>
         available <a  
         href="http://www.cs.dartmouth.edu/gnuplot/gnuplot.html">here</a>.  <p>The program outputs many files. Most of them are files which
     </li>  will be plotted for better understanding.</p>
     <li>'e' opens the <strong>index.htm</strong> file to edit the  
         output files and graphs. </li>  <hr>
     <li>'q' for exiting.</li>  
 </ul>  <h2><a name="output"><font color="#00006A">Output of the program
   and graphs</font> </a></h2>
 <h5><font size="4"><strong>Results files </strong></font><br>  
 <br>  <p>Once the optimization is finished, some graphics can be made
 <font color="#EC5E5E" size="3"><strong>- </strong></font><a  with a grapher. We use Gnuplot which is an interactive plotting
 name="Observed prevalence in each state"><font color="#EC5E5E"  program copyrighted but freely distributed. Imach outputs the
 size="3"><strong>Observed prevalence in each state</strong></font></a><font  source of a gnuplot file, named 'graph.gp', which can be directly
 color="#EC5E5E" size="3"><strong> (and at first pass)</strong></font><b>:  input into gnuplot.<br>
 </b><a href="prbiaspar.txt"><b>prbiaspar.txt</b></a><br>  When the running is finished, the user should enter a caracter
 </h5>  for plotting and output editing. </p>
   
 <p>The first line is the title and displays each field of the  <p>These caracters are:</p>
 file. The first column is age. The fields 2 and 6 are the  
 proportion of individuals in states 1 and 2 respectively as  <ul>
 observed during the first exam. Others fields are the numbers of      <li>'c' to start again the program from the beginning.</li>
 people in states 1, 2 or more. The number of columns increases if      <li>'g' to made graphics. The output graphs are in GIF format
 the number of states is higher than 2.<br>          and you have no control over which is produced. If you
 The header of the file is </p>          want to modify the graphics or make another one, you
           should modify the parameters in the file <b>graph.gp</b>
 <pre># Age Prev(1) N(1) N Age Prev(2) N(2) N          located in imach\bin. A gnuplot reference manual is
 70 1.00000 631 631 70 0.00000 0 631          available <a
 71 0.99681 625 627 71 0.00319 2 627           href="http://www.cs.dartmouth.edu/gnuplot/gnuplot.html">here</a>.
 72 0.97125 1115 1148 72 0.02875 33 1148 </pre>      </li>
       <li>'e' opens the <strong>index.htm</strong> file to edit the
 <pre># Age Prev(1) N(1) N Age Prev(2) N(2) N          output files and graphs. </li>
     70 0.95721 604 631 70 0.04279 27 631</pre>      <li>'q' for exiting.</li>
   </ul>
 <p>It means that at age 70, the prevalence in state 1 is 1.000  
 and in state 2 is 0.00 . At age 71 the number of individuals in  <h5><font size="4"><strong>Results files </strong></font><br>
 state 1 is 625 and in state 2 is 2, hence the total number of  <br>
 people aged 71 is 625+2=627. <br>  <font color="#EC5E5E" size="3"><strong>- </strong></font><a
 </p>  name="Observed prevalence in each state"><font color="#EC5E5E"
   size="3"><strong>Observed prevalence in each state</strong></font></a><font
 <h5><font color="#EC5E5E" size="3"><b>- Estimated parameters and  color="#EC5E5E" size="3"><strong> (and at first pass)</strong></font><b>:
 covariance matrix</b></font><b>: </b><a href="rbiaspar.txt"><b>rbiaspar.txt</b></a></h5>  </b><a href="prbiaspar.txt"><b>prbiaspar.txt</b></a><br>
   </h5>
 <p>This file contains all the maximisation results: </p>  
   <p>The first line is the title and displays each field of the
 <pre> Number of iterations=47  file. The first column is age. The fields 2 and 6 are the
  -2 log likelihood=46553.005854373667    proportion of individuals in states 1 and 2 respectively as
  Estimated parameters: a12 = -12.691743 b12 = 0.095819   observed during the first exam. Others fields are the numbers of
                        a13 = -7.815392   b13 = 0.031851   people in states 1, 2 or more. The number of columns increases if
                        a21 = -1.809895 b21 = -0.030470   the number of states is higher than 2.<br>
                        a23 = -7.838248  b23 = 0.039490    The header of the file is </p>
  Covariance matrix: Var(a12) = 1.03611e-001  
                     Var(b12) = 1.51173e-005  <pre># Age Prev(1) N(1) N Age Prev(2) N(2) N
                     Var(a13) = 1.08952e-001  70 1.00000 631 631 70 0.00000 0 631
                     Var(b13) = 1.68520e-005    71 0.99681 625 627 71 0.00319 2 627
                     Var(a21) = 4.82801e-001  72 0.97125 1115 1148 72 0.02875 33 1148 </pre>
                     Var(b21) = 6.86392e-005  
                     Var(a23) = 2.27587e-001  <pre># Age Prev(1) N(1) N Age Prev(2) N(2) N
                     Var(b23) = 3.04465e-005       70 0.95721 604 631 70 0.04279 27 631</pre>
  </pre>  
   <p>It means that at age 70, the prevalence in state 1 is 1.000
 <h5><font color="#EC5E5E" size="3"><b>- Transition probabilities</b></font><b>:  and in state 2 is 0.00 . At age 71 the number of individuals in
 </b><a href="pijrbiaspar.txt"><b>pijrbiaspar.txt</b></a></h5>  state 1 is 625 and in state 2 is 2, hence the total number of
   people aged 71 is 625+2=627. <br>
 <p>Here are the transitions probabilities Pij(x, x+nh) where nh  </p>
 is a multiple of 2 years. The first column is the starting age x  
 (from age 50 to 100), the second is age (x+nh) and the others are  <h5><font color="#EC5E5E" size="3"><b>- Estimated parameters and
 the transition probabilities p11, p12, p13, p21, p22, p23. For  covariance matrix</b></font><b>: </b><a href="rbiaspar.txt"><b>rbiaspar.txt</b></a></h5>
 example, line 5 of the file is: </p>  
   <p>This file contains all the maximisation results: </p>
 <pre> 100 106 0.03286 0.23512 0.73202 0.02330 0.19210 0.78460 </pre>  
   <pre> Number of iterations=47
 <p>and this means: </p>   -2 log likelihood=46553.005854373667  
    Estimated parameters: a12 = -12.691743 b12 = 0.095819
 <pre>p11(100,106)=0.03286                         a13 = -7.815392   b13 = 0.031851
 p12(100,106)=0.23512                         a21 = -1.809895 b21 = -0.030470
 p13(100,106)=0.73202                         a23 = -7.838248  b23 = 0.039490  
 p21(100,106)=0.02330   Covariance matrix: Var(a12) = 1.03611e-001
 p22(100,106)=0.19210                       Var(b12) = 1.51173e-005
 p22(100,106)=0.78460 </pre>                      Var(a13) = 1.08952e-001
                       Var(b13) = 1.68520e-005  
 <h5><font color="#EC5E5E" size="3"><b>- </b></font><a                      Var(a21) = 4.82801e-001
 name="Stationary prevalence in each state"><font color="#EC5E5E"                      Var(b21) = 6.86392e-005
 size="3"><b>Stationary prevalence in each state</b></font></a><b>:                      Var(a23) = 2.27587e-001
 </b><a href="plrbiaspar.txt"><b>plrbiaspar.txt</b></a></h5>                      Var(b23) = 3.04465e-005
    </pre>
 <pre>#Age 1-1 2-2   
 70 0.92274 0.07726   <h5><font color="#EC5E5E" size="3"><b>- Transition probabilities</b></font><b>:
 71 0.91420 0.08580   </b><a href="pijrbiaspar.txt"><b>pijrbiaspar.txt</b></a></h5>
 72 0.90481 0.09519   
 73 0.89453 0.10547</pre>  <p>Here are the transitions probabilities Pij(x, x+nh) where nh
   is a multiple of 2 years. The first column is the starting age x
 <p>At age 70 the stationary prevalence is 0.92274 in state 1 and  (from age 50 to 100), the second is age (x+nh) and the others are
 0.07726 in state 2. This stationary prevalence differs from  the transition probabilities p11, p12, p13, p21, p22, p23. For
 observed prevalence. Here is the point. The observed prevalence  example, line 5 of the file is: </p>
 at age 70 results from the incidence of disability, incidence of  
 recovery and mortality which occurred in the past of the cohort.  <pre> 100 106 0.03286 0.23512 0.73202 0.02330 0.19210 0.78460 </pre>
 Stationary prevalence results from a simulation with actual  
 incidences and mortality (estimated from this cross-longitudinal  <p>and this means: </p>
 survey). It is the best predictive value of the prevalence in the  
 future if &quot;nothing changes in the future&quot;. This is  <pre>p11(100,106)=0.03286
 exactly what demographers do with a Life table. Life expectancy  p12(100,106)=0.23512
 is the expected mean time to survive if observed mortality rates  p13(100,106)=0.73202
 (incidence of mortality) &quot;remains constant&quot; in the  p21(100,106)=0.02330
 future. </p>  p22(100,106)=0.19210
   p22(100,106)=0.78460 </pre>
 <h5><font color="#EC5E5E" size="3"><b>- Standard deviation of  
 stationary prevalence</b></font><b>: </b><a  <h5><font color="#EC5E5E" size="3"><b>- </b></font><a
 href="vplrbiaspar.txt"><b>vplrbiaspar.txt</b></a></h5>  name="Stationary prevalence in each state"><font color="#EC5E5E"
   size="3"><b>Stationary prevalence in each state</b></font></a><b>:
 <p>The stationary prevalence has to be compared with the observed  </b><a href="plrbiaspar.txt"><b>plrbiaspar.txt</b></a></h5>
 prevalence by age. But both are statistical estimates and  
 subjected to stochastic errors due to the size of the sample, the  <pre>#Age 1-1 2-2
 design of the survey, and, for the stationary prevalence to the  70 0.92274 0.07726
 model used and fitted. It is possible to compute the standard  71 0.91420 0.08580
 deviation of the stationary prevalence at each age.</p>  72 0.90481 0.09519
   73 0.89453 0.10547</pre>
 <h6><font color="#EC5E5E" size="3">Observed and stationary  
 prevalence in state (2=disable) with the confident interval</font>:<b>  <p>At age 70 the stationary prevalence is 0.92274 in state 1 and
 vbiaspar2.gif</b></h6>  0.07726 in state 2. This stationary prevalence differs from
   observed prevalence. Here is the point. The observed prevalence
 <p><br>  at age 70 results from the incidence of disability, incidence of
 This graph exhibits the stationary prevalence in state (2) with  recovery and mortality which occurred in the past of the cohort.
 the confidence interval in red. The green curve is the observed  Stationary prevalence results from a simulation with actual
 prevalence (or proportion of individuals in state (2)). Without  incidences and mortality (estimated from this cross-longitudinal
 discussing the results (it is not the purpose here), we observe  survey). It is the best predictive value of the prevalence in the
 that the green curve is rather below the stationary prevalence.  future if &quot;nothing changes in the future&quot;. This is
 It suggests an increase of the disability prevalence in the  exactly what demographers do with a Life table. Life expectancy
 future.</p>  is the expected mean time to survive if observed mortality rates
   (incidence of mortality) &quot;remains constant&quot; in the
 <p><img src="vbiaspar2.gif" width="400" height="300"></p>  future. </p>
   
 <h6><font color="#EC5E5E" size="3"><b>Convergence to the  <h5><font color="#EC5E5E" size="3"><b>- Standard deviation of
 stationary prevalence of disability</b></font><b>: pbiaspar1.gif</b><br>  stationary prevalence</b></font><b>: </b><a
 <img src="pbiaspar1.gif" width="400" height="300"> </h6>  href="vplrbiaspar.txt"><b>vplrbiaspar.txt</b></a></h5>
   
 <p>This graph plots the conditional transition probabilities from  <p>The stationary prevalence has to be compared with the observed
 an initial state (1=healthy in red at the bottom, or 2=disable in  prevalence by age. But both are statistical estimates and
 green on top) at age <em>x </em>to the final state 2=disable<em> </em>at  subjected to stochastic errors due to the size of the sample, the
 age <em>x+h. </em>Conditional means at the condition to be alive  design of the survey, and, for the stationary prevalence to the
 at age <em>x+h </em>which is <i>hP12x</i> + <em>hP22x</em>. The  model used and fitted. It is possible to compute the standard
 curves <i>hP12x/(hP12x</i> + <em>hP22x) </em>and <i>hP22x/(hP12x</i>  deviation of the stationary prevalence at each age.</p>
 + <em>hP22x) </em>converge with <em>h, </em>to the <em>stationary  
 prevalence of disability</em>. In order to get the stationary  <h6><font color="#EC5E5E" size="3">Observed and stationary
 prevalence at age 70 we should start the process at an earlier  prevalence in state (2=disable) with the confident interval</font>:<b>
 age, i.e.50. If the disability state is defined by severe  vbiaspar2.gif</b></h6>
 disability criteria with only a few chance to recover, then the  
 incidence of recovery is low and the time to convergence is  <p><br>
 probably longer. But we don't have experience yet.</p>  This graph exhibits the stationary prevalence in state (2) with
   the confidence interval in red. The green curve is the observed
 <h5><font color="#EC5E5E" size="3"><b>- Life expectancies by age  prevalence (or proportion of individuals in state (2)). Without
 and initial health status</b></font><b>: </b><a  discussing the results (it is not the purpose here), we observe
 href="erbiaspar.txt"><b>erbiaspar.txt</b></a></h5>  that the green curve is rather below the stationary prevalence.
   It suggests an increase of the disability prevalence in the
 <pre># Health expectancies   future.</p>
 # Age 1-1 1-2 2-1 2-2   
 70 10.7297 2.7809 6.3440 5.9813   <p><img src="vbiaspar2.gif" width="400" height="300"></p>
 71 10.3078 2.8233 5.9295 5.9959   
 72 9.8927 2.8643 5.5305 6.0033   <h6><font color="#EC5E5E" size="3"><b>Convergence to the
 73 9.4848 2.9036 5.1474 6.0035 </pre>  stationary prevalence of disability</b></font><b>: pbiaspar1.gif</b><br>
   <img src="pbiaspar1.gif" width="400" height="300"> </h6>
 <pre>For example 70 10.7297 2.7809 6.3440 5.9813 means:  
 e11=10.7297 e12=2.7809 e21=6.3440 e22=5.9813</pre>  <p>This graph plots the conditional transition probabilities from
   an initial state (1=healthy in red at the bottom, or 2=disable in
 <pre><img src="exbiaspar1.gif" width="400" height="300"><img  green on top) at age <em>x </em>to the final state 2=disable<em> </em>at
 src="exbiaspar2.gif" width="400" height="300"></pre>  age <em>x+h. </em>Conditional means at the condition to be alive
   at age <em>x+h </em>which is <i>hP12x</i> + <em>hP22x</em>. The
 <p>For example, life expectancy of a healthy individual at age 70  curves <i>hP12x/(hP12x</i> + <em>hP22x) </em>and <i>hP22x/(hP12x</i>
 is 10.73 in the healthy state and 2.78 in the disability state  + <em>hP22x) </em>converge with <em>h, </em>to the <em>stationary
 (=13.51 years). If he was disable at age 70, his life expectancy  prevalence of disability</em>. In order to get the stationary
 will be shorter, 6.34 in the healthy state and 5.98 in the  prevalence at age 70 we should start the process at an earlier
 disability state (=12.32 years). The total life expectancy is a  age, i.e.50. If the disability state is defined by severe
 weighted mean of both, 13.51 and 12.32; weight is the proportion  disability criteria with only a few chance to recover, then the
 of people disabled at age 70. In order to get a pure period index  incidence of recovery is low and the time to convergence is
 (i.e. based only on incidences) we use the <a  probably longer. But we don't have experience yet.</p>
 href="#Stationary prevalence in each state">computed or  
 stationary prevalence</a> at age 70 (i.e. computed from  <h5><font color="#EC5E5E" size="3"><b>- Life expectancies by age
 incidences at earlier ages) instead of the <a  and initial health status</b></font><b>: </b><a
 href="#Observed prevalence in each state">observed prevalence</a>  href="erbiaspar.txt"><b>erbiaspar.txt</b></a></h5>
 (for example at first exam) (<a href="#Health expectancies">see  
 below</a>).</p>  <pre># Health expectancies
   # Age 1-1 1-2 2-1 2-2
 <h5><font color="#EC5E5E" size="3"><b>- Variances of life  70 10.7297 2.7809 6.3440 5.9813
 expectancies by age and initial health status</b></font><b>: </b><a  71 10.3078 2.8233 5.9295 5.9959
 href="vrbiaspar.txt"><b>vrbiaspar.txt</b></a></h5>  72 9.8927 2.8643 5.5305 6.0033
   73 9.4848 2.9036 5.1474 6.0035 </pre>
 <p>For example, the covariances of life expectancies Cov(ei,ej)  
 at age 50 are (line 3) </p>  <pre>For example 70 10.7297 2.7809 6.3440 5.9813 means:
   e11=10.7297 e12=2.7809 e21=6.3440 e22=5.9813</pre>
 <pre>   Cov(e1,e1)=0.4667  Cov(e1,e2)=0.0605=Cov(e2,e1)  Cov(e2,e2)=0.0183</pre>  
   <pre><img src="exbiaspar1.gif" width="400" height="300"><img
 <h5><font color="#EC5E5E" size="3"><b>- </b></font><a  src="exbiaspar2.gif" width="400" height="300"></pre>
 name="Health expectancies"><font color="#EC5E5E" size="3"><b>Health  
 expectancies</b></font></a><font color="#EC5E5E" size="3"><b>  <p>For example, life expectancy of a healthy individual at age 70
 with standard errors in parentheses</b></font><b>: </b><a  is 10.73 in the healthy state and 2.78 in the disability state
 href="trbiaspar.txt"><font face="Courier New"><b>trbiaspar.txt</b></font></a></h5>  (=13.51 years). If he was disable at age 70, his life expectancy
   will be shorter, 6.34 in the healthy state and 5.98 in the
 <pre>#Total LEs with variances: e.. (std) e.1 (std) e.2 (std) </pre>  disability state (=12.32 years). The total life expectancy is a
   weighted mean of both, 13.51 and 12.32; weight is the proportion
 <pre>70 13.42 (0.18) 10.39 (0.15) 3.03 (0.10)70 13.81 (0.18) 11.28 (0.14) 2.53 (0.09) </pre>  of people disabled at age 70. In order to get a pure period index
   (i.e. based only on incidences) we use the <a
 <p>Thus, at age 70 the total life expectancy, e..=13.42 years is  href="#Stationary prevalence in each state">computed or
 the weighted mean of e1.=13.51 and e2.=12.32 by the stationary  stationary prevalence</a> at age 70 (i.e. computed from
 prevalence at age 70 which are 0.92274 in state 1 and 0.07726 in  incidences at earlier ages) instead of the <a
 state 2, respectively (the sum is equal to one). e.1=10.39 is the  href="#Observed prevalence in each state">observed prevalence</a>
 Disability-free life expectancy at age 70 (it is again a weighted  (for example at first exam) (<a href="#Health expectancies">see
 mean of e11 and e21). e.2=3.03 is also the life expectancy at age  below</a>).</p>
 70 to be spent in the disability state.</p>  
   <h5><font color="#EC5E5E" size="3"><b>- Variances of life
 <h6><font color="#EC5E5E" size="3"><b>Total life expectancy by  expectancies by age and initial health status</b></font><b>: </b><a
 age and health expectancies in states (1=healthy) and (2=disable)</b></font><b>:  href="vrbiaspar.txt"><b>vrbiaspar.txt</b></a></h5>
 ebiaspar.gif</b></h6>  
   <p>For example, the covariances of life expectancies Cov(ei,ej)
 <p>This figure represents the health expectancies and the total  at age 50 are (line 3) </p>
 life expectancy with the confident interval in dashed curve. </p>  
   <pre>   Cov(e1,e1)=0.4667  Cov(e1,e2)=0.0605=Cov(e2,e1)  Cov(e2,e2)=0.0183</pre>
 <pre>        <img src="ebiaspar.gif" width="400" height="300"></pre>  
   <h5><font color="#EC5E5E" size="3"><b>- </b></font><a
 <p>Standard deviations (obtained from the information matrix of  name="Health expectancies"><font color="#EC5E5E" size="3"><b>Health
 the model) of these quantities are very useful.  expectancies</b></font></a><font color="#EC5E5E" size="3"><b>
 Cross-longitudinal surveys are costly and do not involve huge  with standard errors in parentheses</b></font><b>: </b><a
 samples, generally a few thousands; therefore it is very  href="trbiaspar.txt"><font face="Courier New"><b>trbiaspar.txt</b></font></a></h5>
 important to have an idea of the standard deviation of our  
 estimates. It has been a big challenge to compute the Health  <pre>#Total LEs with variances: e.. (std) e.1 (std) e.2 (std) </pre>
 Expectancy standard deviations. Don't be confuse: life expectancy  
 is, as any expected value, the mean of a distribution; but here  <pre>70 13.42 (0.18) 10.39 (0.15) 3.03 (0.10)70 13.81 (0.18) 11.28 (0.14) 2.53 (0.09) </pre>
 we are not computing the standard deviation of the distribution,  
 but the standard deviation of the estimate of the mean.</p>  <p>Thus, at age 70 the total life expectancy, e..=13.42 years is
   the weighted mean of e1.=13.51 and e2.=12.32 by the stationary
 <p>Our health expectancies estimates vary according to the sample  prevalence at age 70 which are 0.92274 in state 1 and 0.07726 in
 size (and the standard deviations give confidence intervals of  state 2, respectively (the sum is equal to one). e.1=10.39 is the
 the estimate) but also according to the model fitted. Let us  Disability-free life expectancy at age 70 (it is again a weighted
 explain it in more details.</p>  mean of e11 and e21). e.2=3.03 is also the life expectancy at age
   70 to be spent in the disability state.</p>
 <p>Choosing a model means ar least two kind of choices. First we  
 have to decide the number of disability states. Second we have to  <h6><font color="#EC5E5E" size="3"><b>Total life expectancy by
 design, within the logit model family, the model: variables,  age and health expectancies in states (1=healthy) and (2=disable)</b></font><b>:
 covariables, confonding factors etc. to be included.</p>  ebiaspar.gif</b></h6>
   
 <p>More disability states we have, better is our demographical  <p>This figure represents the health expectancies and the total
 approach of the disability process, but smaller are the number of  life expectancy with the confident interval in dashed curve. </p>
 transitions between each state and higher is the noise in the  
 measurement. We do not have enough experiments of the various  <pre>        <img src="ebiaspar.gif" width="400" height="300"></pre>
 models to summarize the advantages and disadvantages, but it is  
 important to say that even if we had huge and unbiased samples,  <p>Standard deviations (obtained from the information matrix of
 the total life expectancy computed from a cross-longitudinal  the model) of these quantities are very useful.
 survey, varies with the number of states. If we define only two  Cross-longitudinal surveys are costly and do not involve huge
 states, alive or dead, we find the usual life expectancy where it  samples, generally a few thousands; therefore it is very
 is assumed that at each age, people are at the same risk to die.  important to have an idea of the standard deviation of our
 If we are differentiating the alive state into healthy and  estimates. It has been a big challenge to compute the Health
 disable, and as the mortality from the disability state is higher  Expectancy standard deviations. Don't be confuse: life expectancy
 than the mortality from the healthy state, we are introducing  is, as any expected value, the mean of a distribution; but here
 heterogeneity in the risk of dying. The total mortality at each  we are not computing the standard deviation of the distribution,
 age is the weighted mean of the mortality in each state by the  but the standard deviation of the estimate of the mean.</p>
 prevalence in each state. Therefore if the proportion of people  
 at each age and in each state is different from the stationary  <p>Our health expectancies estimates vary according to the sample
 equilibrium, there is no reason to find the same total mortality  size (and the standard deviations give confidence intervals of
 at a particular age. Life expectancy, even if it is a very useful  the estimate) but also according to the model fitted. Let us
 tool, has a very strong hypothesis of homogeneity of the  explain it in more details.</p>
 population. Our main purpose is not to measure differential  
 mortality but to measure the expected time in a healthy or  <p>Choosing a model means ar least two kind of choices. First we
 disability state in order to maximise the former and minimize the  have to decide the number of disability states. Second we have to
 latter. But the differential in mortality complexifies the  design, within the logit model family, the model: variables,
 measurement.</p>  covariables, confonding factors etc. to be included.</p>
   
 <p>Incidences of disability or recovery are not affected by the  <p>More disability states we have, better is our demographical
 number of states if these states are independant. But incidences  approach of the disability process, but smaller are the number of
 estimates are dependant on the specification of the model. More  transitions between each state and higher is the noise in the
 covariates we added in the logit model better is the model, but  measurement. We do not have enough experiments of the various
 some covariates are not well measured, some are confounding  models to summarize the advantages and disadvantages, but it is
 factors like in any statistical model. The procedure to &quot;fit  important to say that even if we had huge and unbiased samples,
 the best model' is similar to logistic regression which itself is  the total life expectancy computed from a cross-longitudinal
 similar to regression analysis. We haven't yet been sofar because  survey, varies with the number of states. If we define only two
 we also have a severe limitation which is the speed of the  states, alive or dead, we find the usual life expectancy where it
 convergence. On a Pentium III, 500 MHz, even the simplest model,  is assumed that at each age, people are at the same risk to die.
 estimated by month on 8,000 people may take 4 hours to converge.  If we are differentiating the alive state into healthy and
 Also, the program is not yet a statistical package, which permits  disable, and as the mortality from the disability state is higher
 a simple writing of the variables and the model to take into  than the mortality from the healthy state, we are introducing
 account in the maximisation. The actual program allows only to  heterogeneity in the risk of dying. The total mortality at each
 add simple variables without covariations, like age+sex but  age is the weighted mean of the mortality in each state by the
 without age+sex+ age*sex . This can be done from the source code  prevalence in each state. Therefore if the proportion of people
 (you have to change three lines in the source code) but will  at each age and in each state is different from the stationary
 never be general enough. But what is to remember, is that  equilibrium, there is no reason to find the same total mortality
 incidences or probability of change from one state to another is  at a particular age. Life expectancy, even if it is a very useful
 affected by the variables specified into the model.</p>  tool, has a very strong hypothesis of homogeneity of the
   population. Our main purpose is not to measure differential
 <p>Also, the age range of the people interviewed has a link with  mortality but to measure the expected time in a healthy or
 the age range of the life expectancy which can be estimated by  disability state in order to maximise the former and minimize the
 extrapolation. If your sample ranges from age 70 to 95, you can  latter. But the differential in mortality complexifies the
 clearly estimate a life expectancy at age 70 and trust your  measurement.</p>
 confidence interval which is mostly based on your sample size,  
 but if you want to estimate the life expectancy at age 50, you  <p>Incidences of disability or recovery are not affected by the
 should rely in your model, but fitting a logistic model on a age  number of states if these states are independant. But incidences
 range of 70-95 and estimating probabilties of transition out of  estimates are dependant on the specification of the model. More
 this age range, say at age 50 is very dangerous. At least you  covariates we added in the logit model better is the model, but
 should remember that the confidence interval given by the  some covariates are not well measured, some are confounding
 standard deviation of the health expectancies, are under the  factors like in any statistical model. The procedure to &quot;fit
 strong assumption that your model is the 'true model', which is  the best model' is similar to logistic regression which itself is
 probably not the case.</p>  similar to regression analysis. We haven't yet been sofar because
   we also have a severe limitation which is the speed of the
 <h5><font color="#EC5E5E" size="3"><b>- Copy of the parameter  convergence. On a Pentium III, 500 MHz, even the simplest model,
 file</b></font><b>: </b><a href="orbiaspar.txt"><b>orbiaspar.txt</b></a></h5>  estimated by month on 8,000 people may take 4 hours to converge.
   Also, the program is not yet a statistical package, which permits
 <p>This copy of the parameter file can be useful to re-run the  a simple writing of the variables and the model to take into
 program while saving the old output files. </p>  account in the maximisation. The actual program allows only to
   add simple variables without covariations, like age+sex but
 <hr>  without age+sex+ age*sex . This can be done from the source code
   (you have to change three lines in the source code) but will
 <h2><a name="example" </a><font color="#00006A">Trying an example</font></a></h2>  never be general enough. But what is to remember, is that
   incidences or probability of change from one state to another is
 <p>Since you know how to run the program, it is time to test it  affected by the variables specified into the model.</p>
 on your own computer. Try for example on a parameter file named <a  
 href="file://../mytry/imachpar.txt">imachpar.txt</a> which is a  <p>Also, the age range of the people interviewed has a link with
 copy of <font size="2" face="Courier New">mypar.txt</font>  the age range of the life expectancy which can be estimated by
 included in the subdirectory of imach, <font size="2"  extrapolation. If your sample ranges from age 70 to 95, you can
 face="Courier New">mytry</font>. Edit it to change the name of  clearly estimate a life expectancy at age 70 and trust your
 the data file to <font size="2" face="Courier New">..\data\mydata.txt</font>  confidence interval which is mostly based on your sample size,
 if you don't want to copy it on the same directory. The file <font  but if you want to estimate the life expectancy at age 50, you
 face="Courier New">mydata.txt</font> is a smaller file of 3,000  should rely in your model, but fitting a logistic model on a age
 people but still with 4 waves. </p>  range of 70-95 and estimating probabilties of transition out of
   this age range, say at age 50 is very dangerous. At least you
 <p>Click on the imach.exe icon to open a window. Answer to the  should remember that the confidence interval given by the
 question:'<strong>Enter the parameter file name:'</strong></p>  standard deviation of the health expectancies, are under the
   strong assumption that your model is the 'true model', which is
 <table border="1">  probably not the case.</p>
     <tr>  
         <td width="100%"><strong>IMACH, Version 0.63</strong><p><strong>Enter  <h5><font color="#EC5E5E" size="3"><b>- Copy of the parameter
         the parameter file name: ..\mytry\imachpar.txt</strong></p>  file</b></font><b>: </b><a href="orbiaspar.txt"><b>orbiaspar.txt</b></a></h5>
         </td>  
     </tr>  <p>This copy of the parameter file can be useful to re-run the
 </table>  program while saving the old output files. </p>
   
 <p>Most of the data files or image files generated, will use the  <hr>
 'imachpar' string into their name. The running time is about 2-3  
 minutes on a Pentium III. If the execution worked correctly, the  <h2><a name="example" </a><font color="#00006A">Trying an example</font></a></h2>
 outputs files are created in the current directory, and should be  
 the same as the mypar files initially included in the directory <font  <p>Since you know how to run the program, it is time to test it
 size="2" face="Courier New">mytry</font>.</p>  on your own computer. Try for example on a parameter file named <a
   href="file://../mytry/imachpar.txt">imachpar.txt</a> which is a
 <ul>  copy of <font size="2" face="Courier New">mypar.txt</font>
     <li><pre><u>Output on the screen</u> The output screen looks like <a  included in the subdirectory of imach, <font size="2"
 href="imachrun.LOG">this Log file</a>  face="Courier New">mytry</font>. Edit it to change the name of
 #  the data file to <font size="2" face="Courier New">..\data\mydata.txt</font>
   if you don't want to copy it on the same directory. The file <font
 title=MLE datafile=..\data\mydata.txt lastobs=3000 firstpass=1 lastpass=3  face="Courier New">mydata.txt</font> is a smaller file of 3,000
 ftol=1.000000e-008 stepm=24 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0</pre>  people but still with 4 waves. </p>
     </li>  
     <li><pre>Total number of individuals= 2965, Agemin = 70.00, Agemax= 100.92  <p>Click on the imach.exe icon to open a window. Answer to the
   question:'<strong>Enter the parameter file name:'</strong></p>
 Warning, no any valid information for:126 line=126  
 Warning, no any valid information for:2307 line=2307  <table border="1">
 Delay (in months) between two waves Min=21 Max=51 Mean=24.495826      <tr>
 <font face="Times New Roman">These lines give some warnings on the data file and also some raw statistics on frequencies of transitions.</font>          <td width="100%"><strong>IMACH, Version 0.63</strong><p><strong>Enter
 Age 70 1.=230 loss[1]=3.5% 2.=16 loss[2]=12.5% 1.=222 prev[1]=94.1% 2.=14          the parameter file name: ..\mytry\imachpar.txt</strong></p>
  prev[2]=5.9% 1-1=8 11=200 12=7 13=15 2-1=2 21=6 22=7 23=1          </td>
 Age 102 1.=0 loss[1]=NaNQ% 2.=0 loss[2]=NaNQ% 1.=0 prev[1]=NaNQ% 2.=0 </pre>      </tr>
     </li>  </table>
 </ul>  
   <p>Most of the data files or image files generated, will use the
 <p>&nbsp;</p>  'imachpar' string into their name. The running time is about 2-3
   minutes on a Pentium III. If the execution worked correctly, the
 <ul>  outputs files are created in the current directory, and should be
     <li>Maximisation with the Powell algorithm. 8 directions are  the same as the mypar files initially included in the directory <font
         given corresponding to the 8 parameters. this can be  size="2" face="Courier New">mytry</font>.</p>
         rather long to get convergence.<br>  
         <font size="1" face="Courier New"><br>  <ul>
         Powell iter=1 -2*LL=11531.405658264877 1 0.000000000000 2      <li><pre><u>Output on the screen</u> The output screen looks like <a
         0.000000000000 3<br>  href="imachrun.LOG">this Log file</a>
         0.000000000000 4 0.000000000000 5 0.000000000000 6  #
         0.000000000000 7 <br>  
         0.000000000000 8 0.000000000000<br>  title=MLE datafile=..\data\mydata.txt lastobs=3000 firstpass=1 lastpass=3
         1..........2.................3..........4.................5.........<br>  ftol=1.000000e-008 stepm=24 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0</pre>
         6................7........8...............<br>      </li>
         Powell iter=23 -2*LL=6744.954108371555 1 -12.967632334283      <li><pre>Total number of individuals= 2965, Agemin = 70.00, Agemax= 100.92
         <br>  
         2 0.135136681033 3 -7.402109728262 4 0.067844593326 <br>  Warning, no any valid information for:126 line=126
         5 -0.673601538129 6 -0.006615504377 7 -5.051341616718 <br>  Warning, no any valid information for:2307 line=2307
         8 0.051272038506<br>  Delay (in months) between two waves Min=21 Max=51 Mean=24.495826
         1..............2...........3..............4...........<br>  <font face="Times New Roman">These lines give some warnings on the data file and also some raw statistics on frequencies of transitions.</font>
         5..........6................7...........8.........<br>  Age 70 1.=230 loss[1]=3.5% 2.=16 loss[2]=12.5% 1.=222 prev[1]=94.1% 2.=14
         #Number of iterations = 23, -2 Log likelihood =   prev[2]=5.9% 1-1=8 11=200 12=7 13=15 2-1=2 21=6 22=7 23=1
         6744.954042573691<br>  Age 102 1.=0 loss[1]=NaNQ% 2.=0 loss[2]=NaNQ% 1.=0 prev[1]=NaNQ% 2.=0 </pre>
         # Parameters<br>      </li>
         12 -12.966061 0.135117 <br>  </ul>
         13 -7.401109 0.067831 <br>  
         21 -0.672648 -0.006627 <br>  <p>&nbsp;</p>
         23 -5.051297 0.051271 </font><br>  
         </li>  <ul>
     <li><pre><font size="2">Calculation of the hessian matrix. Wait...      <li>Maximisation with the Powell algorithm. 8 directions are
 12345678.12.13.14.15.16.17.18.23.24.25.26.27.28.34.35.36.37.38.45.46.47.48.56.57.58.67.68.78          given corresponding to the 8 parameters. this can be
           rather long to get convergence.<br>
 Inverting the hessian to get the covariance matrix. Wait...          <font size="1" face="Courier New"><br>
           Powell iter=1 -2*LL=11531.405658264877 1 0.000000000000 2
 #Hessian matrix#          0.000000000000 3<br>
 3.344e+002 2.708e+004 -4.586e+001 -3.806e+003 -1.577e+000 -1.313e+002 3.914e-001 3.166e+001           0.000000000000 4 0.000000000000 5 0.000000000000 6
 2.708e+004 2.204e+006 -3.805e+003 -3.174e+005 -1.303e+002 -1.091e+004 2.967e+001 2.399e+003           0.000000000000 7 <br>
 -4.586e+001 -3.805e+003 4.044e+002 3.197e+004 2.431e-002 1.995e+000 1.783e-001 1.486e+001           0.000000000000 8 0.000000000000<br>
 -3.806e+003 -3.174e+005 3.197e+004 2.541e+006 2.436e+000 2.051e+002 1.483e+001 1.244e+003           1..........2.................3..........4.................5.........<br>
 -1.577e+000 -1.303e+002 2.431e-002 2.436e+000 1.093e+002 8.979e+003 -3.402e+001 -2.843e+003           6................7........8...............<br>
 -1.313e+002 -1.091e+004 1.995e+000 2.051e+002 8.979e+003 7.420e+005 -2.842e+003 -2.388e+005           Powell iter=23 -2*LL=6744.954108371555 1 -12.967632334283
 3.914e-001 2.967e+001 1.783e-001 1.483e+001 -3.402e+001 -2.842e+003 1.494e+002 1.251e+004           <br>
 3.166e+001 2.399e+003 1.486e+001 1.244e+003 -2.843e+003 -2.388e+005 1.251e+004 1.053e+006           2 0.135136681033 3 -7.402109728262 4 0.067844593326 <br>
 # Scales          5 -0.673601538129 6 -0.006615504377 7 -5.051341616718 <br>
 12 1.00000e-004 1.00000e-006          8 0.051272038506<br>
 13 1.00000e-004 1.00000e-006          1..............2...........3..............4...........<br>
 21 1.00000e-003 1.00000e-005          5..........6................7...........8.........<br>
 23 1.00000e-004 1.00000e-005          #Number of iterations = 23, -2 Log likelihood =
 # Covariance          6744.954042573691<br>
   1 5.90661e-001          # Parameters<br>
   2 -7.26732e-003 8.98810e-005          12 -12.966061 0.135117 <br>
   3 8.80177e-002 -1.12706e-003 5.15824e-001          13 -7.401109 0.067831 <br>
   4 -1.13082e-003 1.45267e-005 -6.50070e-003 8.23270e-005          21 -0.672648 -0.006627 <br>
   5 9.31265e-003 -1.16106e-004 6.00210e-004 -8.04151e-006 1.75753e+000          23 -5.051297 0.051271 </font><br>
   6 -1.15664e-004 1.44850e-006 -7.79995e-006 1.04770e-007 -2.12929e-002 2.59422e-004          </li>
   7 1.35103e-003 -1.75392e-005 -6.38237e-004 7.85424e-006 4.02601e-001 -4.86776e-003 1.32682e+000      <li><pre><font size="2">Calculation of the hessian matrix. Wait...
   8 -1.82421e-005 2.35811e-007 7.75503e-006 -9.58687e-008 -4.86589e-003 5.91641e-005 -1.57767e-002 1.88622e-004  12345678.12.13.14.15.16.17.18.23.24.25.26.27.28.34.35.36.37.38.45.46.47.48.56.57.58.67.68.78
 # agemin agemax for lifexpectancy, bage fage (if mle==0 ie no data nor Max likelihood).  
   Inverting the hessian to get the covariance matrix. Wait...
   
 agemin=70 agemax=100 bage=50 fage=100  #Hessian matrix#
 Computing prevalence limit: result on file 'plrmypar.txt'   3.344e+002 2.708e+004 -4.586e+001 -3.806e+003 -1.577e+000 -1.313e+002 3.914e-001 3.166e+001
 Computing pij: result on file 'pijrmypar.txt'   2.708e+004 2.204e+006 -3.805e+003 -3.174e+005 -1.303e+002 -1.091e+004 2.967e+001 2.399e+003
 Computing Health Expectancies: result on file 'ermypar.txt'   -4.586e+001 -3.805e+003 4.044e+002 3.197e+004 2.431e-002 1.995e+000 1.783e-001 1.486e+001
 Computing Variance-covariance of DFLEs: file 'vrmypar.txt'   -3.806e+003 -3.174e+005 3.197e+004 2.541e+006 2.436e+000 2.051e+002 1.483e+001 1.244e+003
 Computing Total LEs with variances: file 'trmypar.txt'   -1.577e+000 -1.303e+002 2.431e-002 2.436e+000 1.093e+002 8.979e+003 -3.402e+001 -2.843e+003
 Computing Variance-covariance of Prevalence limit: file 'vplrmypar.txt'   -1.313e+002 -1.091e+004 1.995e+000 2.051e+002 8.979e+003 7.420e+005 -2.842e+003 -2.388e+005
 End of Imach  3.914e-001 2.967e+001 1.783e-001 1.483e+001 -3.402e+001 -2.842e+003 1.494e+002 1.251e+004
 </font></pre>  3.166e+001 2.399e+003 1.486e+001 1.244e+003 -2.843e+003 -2.388e+005 1.251e+004 1.053e+006
     </li>  # Scales
 </ul>  12 1.00000e-004 1.00000e-006
   13 1.00000e-004 1.00000e-006
 <p><font size="3">Once the running is finished, the program  21 1.00000e-003 1.00000e-005
 requires a caracter:</font></p>  23 1.00000e-004 1.00000e-005
   # Covariance
 <table border="1">    1 5.90661e-001
     <tr>    2 -7.26732e-003 8.98810e-005
         <td width="100%"><strong>Type g for plotting (available    3 8.80177e-002 -1.12706e-003 5.15824e-001
         if mle=1), e to edit output files, c to start again,</strong><p><strong>and    4 -1.13082e-003 1.45267e-005 -6.50070e-003 8.23270e-005
         q for exiting:</strong></p>    5 9.31265e-003 -1.16106e-004 6.00210e-004 -8.04151e-006 1.75753e+000
         </td>    6 -1.15664e-004 1.44850e-006 -7.79995e-006 1.04770e-007 -2.12929e-002 2.59422e-004
     </tr>    7 1.35103e-003 -1.75392e-005 -6.38237e-004 7.85424e-006 4.02601e-001 -4.86776e-003 1.32682e+000
 </table>    8 -1.82421e-005 2.35811e-007 7.75503e-006 -9.58687e-008 -4.86589e-003 5.91641e-005 -1.57767e-002 1.88622e-004
   # agemin agemax for lifexpectancy, bage fage (if mle==0 ie no data nor Max likelihood).
 <p><font size="3">First you should enter <strong>g</strong> to  
 make the figures and then you can edit all the results by typing <strong>e</strong>.  
 </font></p>  agemin=70 agemax=100 bage=50 fage=100
   Computing prevalence limit: result on file 'plrmypar.txt'
 <ul>  Computing pij: result on file 'pijrmypar.txt'
     <li><u>Outputs files</u> <br>  Computing Health Expectancies: result on file 'ermypar.txt'
         - index.htm, this file is the master file on which you  Computing Variance-covariance of DFLEs: file 'vrmypar.txt'
         should click first.<br>  Computing Total LEs with variances: file 'trmypar.txt'
         - Observed prevalence in each state: <a  Computing Variance-covariance of Prevalence limit: file 'vplrmypar.txt'
         href="..\mytry\prmypar.txt">mypar.txt</a> <br>  End of Imach
         - Estimated parameters and the covariance matrix: <a  </font></pre>
         href="..\mytry\rmypar.txt">rmypar.txt</a> <br>      </li>
         - Stationary prevalence in each state: <a  </ul>
         href="..\mytry\plrmypar.txt">plrmypar.txt</a> <br>  
         - Transition probabilities: <a  <p><font size="3">Once the running is finished, the program
         href="..\mytry\pijrmypar.txt">pijrmypar.txt</a> <br>  requires a caracter:</font></p>
         - Copy of the parameter file: <a  
         href="..\mytry\ormypar.txt">ormypar.txt</a> <br>  <table border="1">
         - Life expectancies by age and initial health status: <a      <tr>
         href="..\mytry\ermypar.txt">ermypar.txt</a> <br>          <td width="100%"><strong>Type g for plotting (available
         - Variances of life expectancies by age and initial          if mle=1), e to edit output files, c to start again,</strong><p><strong>and
         health status: <a href="..\mytry\vrmypar.txt">vrmypar.txt</a>          q for exiting:</strong></p>
         <br>          </td>
         - Health expectancies with their variances: <a      </tr>
         href="..\mytry\trmypar.txt">trmypar.txt</a> <br>  </table>
         - Standard deviation of stationary prevalence: <a  
         href="..\mytry\vplrmypar.txt">vplrmypar.txt</a> <br>  <p><font size="3">First you should enter <strong>g</strong> to
         <br>  make the figures and then you can edit all the results by typing <strong>e</strong>.
         </li>  </font></p>
     <li><u>Graphs</u> <br>  
         <br>  <ul>
         -<a href="..\mytry\vmypar1.gif">Observed and stationary      <li><u>Outputs files</u> <br>
         prevalence in state (1) with the confident interval</a> <br>          - index.htm, this file is the master file on which you
         -<a href="..\mytry\vmypar2.gif">Observed and stationary          should click first.<br>
         prevalence in state (2) with the confident interval</a> <br>          - Observed prevalence in each state: <a
         -<a href="..\mytry\exmypar1.gif">Health life expectancies          href="..\mytry\prmypar.txt">mypar.txt</a> <br>
         by age and initial health state (1)</a> <br>          - Estimated parameters and the covariance matrix: <a
         -<a href="..\mytry\exmypar2.gif">Health life expectancies          href="..\mytry\rmypar.txt">rmypar.txt</a> <br>
         by age and initial health state (2)</a> <br>          - Stationary prevalence in each state: <a
         -<a href="..\mytry\emypar.gif">Total life expectancy by          href="..\mytry\plrmypar.txt">plrmypar.txt</a> <br>
         age and health expectancies in states (1) and (2).</a> </li>          - Transition probabilities: <a
 </ul>          href="..\mytry\pijrmypar.txt">pijrmypar.txt</a> <br>
           - Copy of the parameter file: <a
 <p>This software have been partly granted by <a          href="..\mytry\ormypar.txt">ormypar.txt</a> <br>
 href="http://euroreves.ined.fr">Euro-REVES</a>, a concerted          - Life expectancies by age and initial health status: <a
 action from the European Union. It will be copyrighted          href="..\mytry\ermypar.txt">ermypar.txt</a> <br>
 identically to a GNU software product, i.e. program and software          - Variances of life expectancies by age and initial
 can be distributed freely for non commercial use. Sources are not          health status: <a href="..\mytry\vrmypar.txt">vrmypar.txt</a>
 widely distributed today. You can get them by asking us with a          <br>
 simple justification (name, email, institute) <a          - Health expectancies with their variances: <a
 href="mailto:brouard@ined.fr">mailto:brouard@ined.fr</a> and <a          href="..\mytry\trmypar.txt">trmypar.txt</a> <br>
 href="mailto:lievre@ined.fr">mailto:lievre@ined.fr</a> .</p>          - Standard deviation of stationary prevalence: <a
           href="..\mytry\vplrmypar.txt">vplrmypar.txt</a> <br>
 <p>Latest version (0.63 of 16 march 2000) can be accessed at <a          <br>
 href="http://euroeves.ined.fr/imach">http://euroreves.ined.fr/imach</a><br>          </li>
 </p>      <li><u>Graphs</u> <br>
 </body>          <br>
 </html>          -<a href="..\mytry\vmypar1.gif">Observed and stationary
           prevalence in state (1) with the confident interval</a> <br>
           -<a href="..\mytry\vmypar2.gif">Observed and stationary
           prevalence in state (2) with the confident interval</a> <br>
           -<a href="..\mytry\exmypar1.gif">Health life expectancies
           by age and initial health state (1)</a> <br>
           -<a href="..\mytry\exmypar2.gif">Health life expectancies
           by age and initial health state (2)</a> <br>
           -<a href="..\mytry\emypar.gif">Total life expectancy by
           age and health expectancies in states (1) and (2).</a> </li>
   </ul>
   
   <p>This software have been partly granted by <a
   href="http://euroreves.ined.fr">Euro-REVES</a>, a concerted
   action from the European Union. It will be copyrighted
   identically to a GNU software product, i.e. program and software
   can be distributed freely for non commercial use. Sources are not
   widely distributed today. You can get them by asking us with a
   simple justification (name, email, institute) <a
   href="mailto:brouard@ined.fr">mailto:brouard@ined.fr</a> and <a
   href="mailto:lievre@ined.fr">mailto:lievre@ined.fr</a> .</p>
   
   <p>Latest version (0.63 of 16 march 2000) can be accessed at <a
   href="http://euroeves.ined.fr/imach">http://euroreves.ined.fr/imach</a><br>
   </p>
   </body>
   </html>

Removed from v.1.1  
changed lines
  Added in v.1.2


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