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        !             7: <title>Computing Health Expectancies using IMaCh</title>
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        !            13: 
        !            14: <h1 align="center"><font color="#00006A">Computing Health
        !            15: Expectancies using IMaCh</font></h1>
        !            16: 
        !            17: <h1 align="center"><font color="#00006A" size="5">(a Maximum
        !            18: Likelihood Computer Program using Interpolation of Markov Chains)</font></h1>
        !            19: 
        !            20: <p align="center">&nbsp;</p>
        !            21: 
        !            22: <p align="center"><a href="http://www.ined.fr/"><img
        !            23: src="logo-ined.gif" border="0" width="151" height="76"></a><img
        !            24: src="euroreves2.gif" width="151" height="75"></p>
        !            25: 
        !            26: <h3 align="center"><a href="http://www.ined.fr/"><font
        !            27: color="#00006A">INED</font></a><font color="#00006A"> and </font><a
        !            28: href="http://euroreves.ined.fr"><font color="#00006A">EUROREVES</font></a></h3>
        !            29: 
        !            30: <p align="center"><font color="#00006A" size="4"><strong>March
        !            31: 2000</strong></font></p>
        !            32: 
        !            33: <hr size="3" color="#EC5E5E">
        !            34: 
        !            35: <p align="center"><font color="#00006A"><strong>Authors of the
        !            36: program: </strong></font><a href="http://sauvy.ined.fr/brouard"><font
        !            37: color="#00006A"><strong>Nicolas Brouard</strong></font></a><font
        !            38: color="#00006A"><strong>, senior researcher at the </strong></font><a
        !            39: href="http://www.ined.fr"><font color="#00006A"><strong>Institut
        !            40: National d'Etudes Démographiques</strong></font></a><font
        !            41: color="#00006A"><strong> (INED, Paris) in the &quot;Mortality,
        !            42: Health and Epidemiology&quot; Research Unit </strong></font></p>
        !            43: 
        !            44: <p align="center"><font color="#00006A"><strong>and Agnès
        !            45: Lièvre<br clear="left">
        !            46: </strong></font></p>
        !            47: 
        !            48: <h4><font color="#00006A">Contribution to the mathematics: C. R.
        !            49: Heathcote </font><font color="#00006A" size="2">(Australian
        !            50: National University, Canberra).</font></h4>
        !            51: 
        !            52: <h4><font color="#00006A">Contact: Agnès Lièvre (</font><a
        !            53: href="mailto:lievre@ined.fr"><font color="#00006A"><i>lievre@ined.fr</i></font></a><font
        !            54: color="#00006A">) </font></h4>
        !            55: 
        !            56: <hr>
        !            57: 
        !            58: <ul>
        !            59:     <li><a href="#intro">Introduction</a> </li>
        !            60:     <li>The detailed statistical model (<a href="docmath.pdf">PDF
        !            61:         version</a>),(<a href="docmath.ps">ps version</a>) </li>
        !            62:     <li><a href="#data">On what kind of data can it be used?</a></li>
        !            63:     <li><a href="#datafile">The data file</a> </li>
        !            64:     <li><a href="#biaspar">The parameter file</a> </li>
        !            65:     <li><a href="#running">Running Imach</a> </li>
        !            66:     <li><a href="#output">Output files and graphs</a> </li>
        !            67:     <li><a href="#example">Exemple</a> </li>
        !            68: </ul>
        !            69: 
        !            70: <hr>
        !            71: 
        !            72: <h2><a name="intro"><font color="#00006A">Introduction</font></a></h2>
        !            73: 
        !            74: <p>This program computes <b>Healthy Life Expectancies</b> from <b>cross-longitudinal
        !            75: data</b>. Within the family of Health Expectancies (HE),
        !            76: Disability-free life expectancy (DFLE) is probably the most
        !            77: important index to monitor. In low mortality countries, there is
        !            78: a fear that when mortality declines, the increase in DFLE is not
        !            79: proportionate to the increase in total Life expectancy. This case
        !            80: is called the <em>Expansion of morbidity</em>. Most of the data
        !            81: collected today, in particular by the international <a
        !            82: href="http://euroreves/reves">REVES</a> network on Health
        !            83: expectancy, and most HE indices based on these data, are <em>cross-sectional</em>.
        !            84: It means that the information collected comes from a single
        !            85: cross-sectional survey: people from various ages (but mostly old
        !            86: people) are surveyed on their health status at a single date.
        !            87: Proportion of people disabled at each age, can then be measured
        !            88: at that date. This age-specific prevalence curve is then used to
        !            89: distinguish, within the stationary population (which, by
        !            90: definition, is the life table estimated from the vital statistics
        !            91: on mortality at the same date), the disable population from the
        !            92: disability-free population. Life expectancy (LE) (or total
        !            93: population divided by the yearly number of births or deaths of
        !            94: this stationary population) is then decomposed into DFLE and DLE.
        !            95: This method of computing HE is usually called the Sullivan method
        !            96: (from the name of the author who first described it).</p>
        !            97: 
        !            98: <p>Age-specific proportions of people disable are very difficult
        !            99: to forecast because each proportion corresponds to historical
        !           100: conditions of the cohort and it is the result of the historical
        !           101: flows from entering disability and recovering in the past until
        !           102: today. The age-specific intensities (or incidence rates) of
        !           103: entering disability or recovering a good health, are reflecting
        !           104: actual conditions and therefore can be used at each age to
        !           105: forecast the future of this cohort. For example if a country is
        !           106: improving its technology of prosthesis, the incidence of
        !           107: recovering the ability to walk will be higher at each (old) age,
        !           108: but the prevalence of disability will only slightly reflect an
        !           109: improve because the prevalence is mostly affected by the history
        !           110: of the cohort and not by recent period effects. To measure the
        !           111: period improvement we have to simulate the future of a cohort of
        !           112: new-borns entering or leaving at each age the disability state or
        !           113: dying according to the incidence rates measured today on
        !           114: different cohorts. The proportion of people disabled at each age
        !           115: in this simulated cohort will be much lower (using the exemple of
        !           116: an improvement) that the proportions observed at each age in a
        !           117: cross-sectional survey. This new prevalence curve introduced in a
        !           118: life table will give a much more actual and realistic HE level
        !           119: than the Sullivan method which mostly measured the History of
        !           120: health conditions in this country.</p>
        !           121: 
        !           122: <p>Therefore, the main question is how to measure incidence rates
        !           123: from cross-longitudinal surveys? This is the goal of the IMaCH
        !           124: program. From your data and using IMaCH you can estimate period
        !           125: HE and not only Sullivan's HE. Also the standard errors of the HE
        !           126: are computed.</p>
        !           127: 
        !           128: <p>A cross-longitudinal survey consists in a first survey
        !           129: (&quot;cross&quot;) where individuals from different ages are
        !           130: interviewed on their health status or degree of disability. At
        !           131: least a second wave of interviews (&quot;longitudinal&quot;)
        !           132: should measure each new individual health status. Health
        !           133: expectancies are computed from the transitions observed between
        !           134: waves and are computed for each degree of severity of disability
        !           135: (number of life states). More degrees you consider, more time is
        !           136: necessary to reach the Maximum Likelihood of the parameters
        !           137: involved in the model. Considering only two states of disability
        !           138: (disable and healthy) is generally enough but the computer
        !           139: program works also with more health statuses.<br>
        !           140: <br>
        !           141: The simplest model is the multinomial logistic model where <i>pij</i>
        !           142: is the probability to be observed in state <i>j</i> at the second
        !           143: wave conditional to be observed in state <em>i</em> at the first
        !           144: wave. Therefore a simple model is: log<em>(pij/pii)= aij +
        !           145: bij*age+ cij*sex,</em> where '<i>age</i>' is age and '<i>sex</i>'
        !           146: is a covariate. The advantage that this computer program claims,
        !           147: comes from that if the delay between waves is not identical for
        !           148: each individual, or if some individual missed an interview, the
        !           149: information is not rounded or lost, but taken into account using
        !           150: an interpolation or extrapolation. <i>hPijx</i> is the
        !           151: probability to be observed in state <i>i</i> at age <i>x+h</i>
        !           152: conditional to the observed state <i>i</i> at age <i>x</i>. The
        !           153: delay '<i>h</i>' can be split into an exact number (<i>nh*stepm</i>)
        !           154: of unobserved intermediate states. This elementary transition (by
        !           155: month or quarter trimester, semester or year) is modeled as a
        !           156: multinomial logistic. The <i>hPx</i> matrix is simply the matrix
        !           157: product of <i>nh*stepm</i> elementary matrices and the
        !           158: contribution of each individual to the likelihood is simply <i>hPijx</i>.
        !           159: <br>
        !           160: </p>
        !           161: 
        !           162: <p>The program presented in this manual is a quite general
        !           163: program named <strong>IMaCh</strong> (for <strong>I</strong>nterpolated
        !           164: <strong>MA</strong>rkov <strong>CH</strong>ain), designed to
        !           165: analyse transition data from longitudinal surveys. The first step
        !           166: is the parameters estimation of a transition probabilities model
        !           167: between an initial status and a final status. From there, the
        !           168: computer program produces some indicators such as observed and
        !           169: stationary prevalence, life expectancies and their variances and
        !           170: graphs. Our transition model consists in absorbing and
        !           171: non-absorbing states with the possibility of return across the
        !           172: non-absorbing states. The main advantage of this package,
        !           173: compared to other programs for the analysis of transition data
        !           174: (For example: Proc Catmod of SAS<sup>®</sup>) is that the whole
        !           175: individual information is used even if an interview is missing, a
        !           176: status or a date is unknown or when the delay between waves is
        !           177: not identical for each individual. The program can be executed
        !           178: according to parameters: selection of a sub-sample, number of
        !           179: absorbing and non-absorbing states, number of waves taken in
        !           180: account (the user inputs the first and the last interview), a
        !           181: tolerance level for the maximization function, the periodicity of
        !           182: the transitions (we can compute annual, quaterly or monthly
        !           183: transitions), covariates in the model. It works on Windows or on
        !           184: Unix.<br>
        !           185: </p>
        !           186: 
        !           187: <hr>
        !           188: 
        !           189: <h2><a name="data"><font color="#00006A">On what kind of data can
        !           190: it be used?</font></a></h2>
        !           191: 
        !           192: <p>The minimum data required for a transition model is the
        !           193: recording of a set of individuals interviewed at a first date and
        !           194: interviewed again at least one another time. From the
        !           195: observations of an individual, we obtain a follow-up over time of
        !           196: the occurrence of a specific event. In this documentation, the
        !           197: event is related to health status at older ages, but the program
        !           198: can be applied on a lot of longitudinal studies in different
        !           199: contexts. To build the data file explained into the next section,
        !           200: you must have the month and year of each interview and the
        !           201: corresponding health status. But in order to get age, date of
        !           202: birth (month and year) is required (missing values is allowed for
        !           203: month). Date of death (month and year) is an important
        !           204: information also required if the individual is dead. Shorter
        !           205: steps (i.e. a month) will more closely take into account the
        !           206: survival time after the last interview.</p>
        !           207: 
        !           208: <hr>
        !           209: 
        !           210: <h2><a name="datafile"><font color="#00006A">The data file</font></a></h2>
        !           211: 
        !           212: <p>In this example, 8,000 people have been interviewed in a
        !           213: cross-longitudinal survey of 4 waves (1984, 1986, 1988, 1990).
        !           214: Some people missed 1, 2 or 3 interviews. Health statuses are
        !           215: healthy (1) and disable (2). The survey is not a real one. It is
        !           216: a simulation of the American Longitudinal Survey on Aging. The
        !           217: disability state is defined if the individual missed one of four
        !           218: ADL (Activity of daily living, like bathing, eating, walking).
        !           219: Therefore, even is the individuals interviewed in the sample are
        !           220: virtual, the information brought with this sample is close to the
        !           221: situation of the United States. Sex is not recorded is this
        !           222: sample.</p>
        !           223: 
        !           224: <p>Each line of the data set (named <a href="data1.txt">data1.txt</a>
        !           225: in this first example) is an individual record which fields are: </p>
        !           226: 
        !           227: <ul>
        !           228:     <li><b>Index number</b>: positive number (field 1) </li>
        !           229:     <li><b>First covariate</b> positive number (field 2) </li>
        !           230:     <li><b>Second covariate</b> positive number (field 3) </li>
        !           231:     <li><a name="Weight"><b>Weight</b></a>: positive number
        !           232:         (field 4) . In most surveys individuals are weighted
        !           233:         according to the stratification of the sample.</li>
        !           234:     <li><b>Date of birth</b>: coded as mm/yyyy. Missing dates are
        !           235:         coded as 99/9999 (field 5) </li>
        !           236:     <li><b>Date of death</b>: coded as mm/yyyy. Missing dates are
        !           237:         coded as 99/9999 (field 6) </li>
        !           238:     <li><b>Date of first interview</b>: coded as mm/yyyy. Missing
        !           239:         dates are coded as 99/9999 (field 7) </li>
        !           240:     <li><b>Status at first interview</b>: positive number.
        !           241:         Missing values ar coded -1. (field 8) </li>
        !           242:     <li><b>Date of second interview</b>: coded as mm/yyyy.
        !           243:         Missing dates are coded as 99/9999 (field 9) </li>
        !           244:     <li><strong>Status at second interview</strong> positive
        !           245:         number. Missing values ar coded -1. (field 10) </li>
        !           246:     <li><b>Date of third interview</b>: coded as mm/yyyy. Missing
        !           247:         dates are coded as 99/9999 (field 11) </li>
        !           248:     <li><strong>Status at third interview</strong> positive
        !           249:         number. Missing values ar coded -1. (field 12) </li>
        !           250:     <li><b>Date of fourth interview</b>: coded as mm/yyyy.
        !           251:         Missing dates are coded as 99/9999 (field 13) </li>
        !           252:     <li><strong>Status at fourth interview</strong> positive
        !           253:         number. Missing values are coded -1. (field 14) </li>
        !           254:     <li>etc</li>
        !           255: </ul>
        !           256: 
        !           257: <p>&nbsp;</p>
        !           258: 
        !           259: <p>If your longitudinal survey do not include information about
        !           260: weights or covariates, you must fill the column with a number
        !           261: (e.g. 1) because a missing field is not allowed.</p>
        !           262: 
        !           263: <hr>
        !           264: 
        !           265: <h2><font color="#00006A">Your first example parameter file</font><a
        !           266: href="http://euroreves.ined.fr/imach"></a><a name="uio"></a></h2>
        !           267: 
        !           268: <h2><a name="biaspar"></a>#Imach version 0.63, February 2000,
        !           269: INED-EUROREVES </h2>
        !           270: 
        !           271: <p>This is a comment. Comments start with a '#'.</p>
        !           272: 
        !           273: <h4><font color="#FF0000">First uncommented line</font></h4>
        !           274: 
        !           275: <pre>title=1st_example datafile=data1.txt lastobs=8600 firstpass=1 lastpass=4</pre>
        !           276: 
        !           277: <ul>
        !           278:     <li><b>title=</b> 1st_example is title of the run. </li>
        !           279:     <li><b>datafile=</b>data1.txt is the name of the data set.
        !           280:         Our example is a six years follow-up survey. It consists
        !           281:         in a baseline followed by 3 reinterviews. </li>
        !           282:     <li><b>lastobs=</b> 8600 the program is able to run on a
        !           283:         subsample where the last observation number is lastobs.
        !           284:         It can be set a bigger number than the real number of
        !           285:         observations (e.g. 100000). In this example, maximisation
        !           286:         will be done on the 8600 first records. </li>
        !           287:     <li><b>firstpass=1</b> , <b>lastpass=4 </b>In case of more
        !           288:         than two interviews in the survey, the program can be run
        !           289:         on selected transitions periods. firstpass=1 means the
        !           290:         first interview included in the calculation is the
        !           291:         baseline survey. lastpass=4 means that the information
        !           292:         brought by the 4th interview is taken into account.</li>
        !           293: </ul>
        !           294: 
        !           295: <p>&nbsp;</p>
        !           296: 
        !           297: <h4><a name="biaspar-2"><font color="#FF0000">Second uncommented
        !           298: line</font></a></h4>
        !           299: 
        !           300: <pre>ftol=1.e-08 stepm=1 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0</pre>
        !           301: 
        !           302: <ul>
        !           303:     <li><b>ftol=1e-8</b> Convergence tolerance on the function
        !           304:         value in the maximisation of the likelihood. Choosing a
        !           305:         correct value for ftol is difficult. 1e-8 is a correct
        !           306:         value for a 32 bits computer.</li>
        !           307:     <li><b>stepm=1</b> Time unit in months for interpolation.
        !           308:         Examples:<ul>
        !           309:             <li>If stepm=1, the unit is a month </li>
        !           310:             <li>If stepm=4, the unit is a trimester</li>
        !           311:             <li>If stepm=12, the unit is a year </li>
        !           312:             <li>If stepm=24, the unit is two years</li>
        !           313:             <li>... </li>
        !           314:         </ul>
        !           315:     </li>
        !           316:     <li><b>ncov=2</b> Number of covariates to be add to the
        !           317:         model. The intercept and the age parameter are counting
        !           318:         for 2 covariates. For example, if you want to add gender
        !           319:         in the covariate vector you must write ncov=3 else
        !           320:         ncov=2. </li>
        !           321:     <li><b>nlstate=2</b> Number of non-absorbing (live) states.
        !           322:         Here we have two alive states: disability-free is coded 1
        !           323:         and disability is coded 2. </li>
        !           324:     <li><b>ndeath=1</b> Number of absorbing states. The absorbing
        !           325:         state death is coded 3. </li>
        !           326:     <li><b>maxwav=4</b> Maximum number of waves. The program can
        !           327:         not include more than 4 interviews. </li>
        !           328:     <li><a name="mle"><b>mle</b></a><b>=1</b> Option for the
        !           329:         Maximisation Likelihood Estimation. <ul>
        !           330:             <li>If mle=1 the program does the maximisation and
        !           331:                 the calculation of heath expectancies </li>
        !           332:             <li>If mle=0 the program only does the calculation of
        !           333:                 the health expectancies. </li>
        !           334:         </ul>
        !           335:     </li>
        !           336:     <li><b>weight=0</b> Possibility to add weights. <ul>
        !           337:             <li>If weight=0 no weights are included </li>
        !           338:             <li>If weight=1 the maximisation integrates the
        !           339:                 weights which are in field <a href="#Weight">4</a></li>
        !           340:         </ul>
        !           341:     </li>
        !           342: </ul>
        !           343: 
        !           344: <h4><font color="#FF0000">Guess values for optimization</font><font
        !           345: color="#00006A"> </font></h4>
        !           346: 
        !           347: <p>You must write the initial guess values of the parameters for
        !           348: optimization. The number of parameters, <em>N</em> depends on the
        !           349: number of absorbing states and non-absorbing states and on the
        !           350: number of covariates. <br>
        !           351: <em>N</em> is given by the formula <em>N</em>=(<em>nlstate</em> +
        !           352: <em>ndeath</em>-1)*<em>nlstate</em>*<em>ncov</em>&nbsp;. <br>
        !           353: <br>
        !           354: Thus in the simple case with 2 covariates (the model is log
        !           355: (pij/pii) = aij + bij * age where intercept and age are the two
        !           356: covariates), and 2 health degrees (1 for disability-free and 2
        !           357: for disability) and 1 absorbing state (3), you must enter 8
        !           358: initials values, a12, b12, a13, b13, a21, b21, a23, b23. You can
        !           359: start with zeros as in this example, but if you have a more
        !           360: precise set (for example from an earlier run) you can enter it
        !           361: and it will speed up them<br>
        !           362: Each of the four lines starts with indices &quot;ij&quot;: <br>
        !           363: <br>
        !           364: <b>ij aij bij</b> </p>
        !           365: 
        !           366: <blockquote>
        !           367:     <pre># Guess values of aij and bij in log (pij/pii) = aij + bij * age
        !           368: 12 -14.155633  0.110794 
        !           369: 13  -7.925360  0.032091 
        !           370: 21  -1.890135 -0.029473 
        !           371: 23  -6.234642  0.022315 </pre>
        !           372: </blockquote>
        !           373: 
        !           374: <p>or, to simplify: </p>
        !           375: 
        !           376: <blockquote>
        !           377:     <pre>12 0.0 0.0
        !           378: 13 0.0 0.0
        !           379: 21 0.0 0.0
        !           380: 23 0.0 0.0</pre>
        !           381: </blockquote>
        !           382: 
        !           383: <h4><font color="#FF0000">Guess values for computing variances</font></h4>
        !           384: 
        !           385: <p>This is an output if <a href="#mle">mle</a>=1. But it can be
        !           386: used as an input to get the vairous output data files (Health
        !           387: expectancies, stationary prevalence etc.) and figures without
        !           388: rerunning the rather long maximisation phase (mle=0). </p>
        !           389: 
        !           390: <p>The scales are small values for the evaluation of numerical
        !           391: derivatives. These derivatives are used to compute the hessian
        !           392: matrix of the parameters, that is the inverse of the covariance
        !           393: matrix, and the variances of health expectancies. Each line
        !           394: consists in indices &quot;ij&quot; followed by the initial scales
        !           395: (zero to simplify) associated with aij and bij. </p>
        !           396: 
        !           397: <ul>
        !           398:     <li>If mle=1 you can enter zeros:</li>
        !           399: </ul>
        !           400: 
        !           401: <blockquote>
        !           402:     <pre># Scales (for hessian or gradient estimation)
        !           403: 12 0. 0. 
        !           404: 13 0. 0. 
        !           405: 21 0. 0. 
        !           406: 23 0. 0. </pre>
        !           407: </blockquote>
        !           408: 
        !           409: <ul>
        !           410:     <li>If mle=0 you must enter a covariance matrix (usually
        !           411:         obtained from an earlier run).</li>
        !           412: </ul>
        !           413: 
        !           414: <h4><font color="#FF0000">Covariance matrix of parameters</font></h4>
        !           415: 
        !           416: <p>This is an output if <a href="#mle">mle</a>=1. But it can be
        !           417: used as an input to get the vairous output data files (Health
        !           418: expectancies, stationary prevalence etc.) and figures without
        !           419: rerunning the rather long maximisation phase (mle=0). </p>
        !           420: 
        !           421: <p>Each line starts with indices &quot;ijk&quot; followed by the
        !           422: covariances between aij and bij: </p>
        !           423: 
        !           424: <pre>
        !           425:    121 Var(a12) 
        !           426:    122 Cov(b12,a12)  Var(b12) 
        !           427:           ...
        !           428:    232 Cov(b23,a12)  Cov(b23,b12) ... Var (b23) </pre>
        !           429: 
        !           430: <ul>
        !           431:     <li>If mle=1 you can enter zeros. </li>
        !           432: </ul>
        !           433: 
        !           434: <blockquote>
        !           435:     <pre># Covariance matrix
        !           436: 121 0.
        !           437: 122 0. 0.
        !           438: 131 0. 0. 0. 
        !           439: 132 0. 0. 0. 0. 
        !           440: 211 0. 0. 0. 0. 0. 
        !           441: 212 0. 0. 0. 0. 0. 0. 
        !           442: 231 0. 0. 0. 0. 0. 0. 0. 
        !           443: 232 0. 0. 0. 0. 0. 0. 0. 0.</pre>
        !           444: </blockquote>
        !           445: 
        !           446: <ul>
        !           447:     <li>If mle=0 you must enter a covariance matrix (usually
        !           448:         obtained from an earlier run).<br>
        !           449:         </li>
        !           450: </ul>
        !           451: 
        !           452: <h4><a name="biaspar-l"></a><font color="#FF0000">last
        !           453: uncommented line</font></h4>
        !           454: 
        !           455: <pre>agemin=70 agemax=100 bage=50 fage=100</pre>
        !           456: 
        !           457: <p>Once we obtained the estimated parameters, the program is able
        !           458: to calculated stationary prevalence, transitions probabilities
        !           459: and life expectancies at any age. Choice of age ranges is useful
        !           460: for extrapolation. In our data file, ages varies from age 70 to
        !           461: 102. Setting bage=50 and fage=100, makes the program computing
        !           462: life expectancy from age bage to age fage. As we use a model, we
        !           463: can compute life expectancy on a wider age range than the age
        !           464: range from the data. But the model can be rather wrong on big
        !           465: intervals.</p>
        !           466: 
        !           467: <p>Similarly, it is possible to get extrapolated stationary
        !           468: prevalence by age raning from agemin to agemax. </p>
        !           469: 
        !           470: <ul>
        !           471:     <li><b>agemin=</b> Minimum age for calculation of the
        !           472:         stationary prevalence </li>
        !           473:     <li><b>agemax=</b> Maximum age for calculation of the
        !           474:         stationary prevalence </li>
        !           475:     <li><b>bage=</b> Minimum age for calculation of the health
        !           476:         expectancies </li>
        !           477:     <li><b>fage=</b> Maximum ages for calculation of the health
        !           478:         expectancies </li>
        !           479: </ul>
        !           480: 
        !           481: <hr>
        !           482: 
        !           483: <h2><a name="running"></a><font color="#00006A">Running Imach
        !           484: with this example</font></h2>
        !           485: 
        !           486: <p>We assume that you entered your <a href="biaspar.txt">1st_example
        !           487: parameter file</a> as explained <a href="#biaspar">above</a>. To
        !           488: run the program you should click on the imach.exe icon and enter
        !           489: the name of the parameter file which is for example <a
        !           490: href="C:\usr\imach\mle\biaspar.txt">C:\usr\imach\mle\biaspar.txt</a>
        !           491: (you also can click on the biaspar.txt icon located in <br>
        !           492: <a href="C:\usr\imach\mle">C:\usr\imach\mle</a> and put it with
        !           493: the mouse on the imach window).<br>
        !           494: </p>
        !           495: 
        !           496: <p>The time to converge depends on the step unit that you used (1
        !           497: month is cpu consuming), on the number of cases, and on the
        !           498: number of variables.</p>
        !           499: 
        !           500: <p>The program outputs many files. Most of them are files which
        !           501: will be plotted for better understanding.</p>
        !           502: 
        !           503: <hr>
        !           504: 
        !           505: <h2><a name="output"><font color="#00006A">Output of the program
        !           506: and graphs</font> </a></h2>
        !           507: 
        !           508: <p>Once the optimization is finished, some graphics can be made
        !           509: with a grapher. We use Gnuplot which is an interactive plotting
        !           510: program copyrighted but freely distributed. Imach outputs the
        !           511: source of a gnuplot file, named 'graph.gp', which can be directly
        !           512: input into gnuplot.<br>
        !           513: When the running is finished, the user should enter a caracter
        !           514: for plotting and output editing. </p>
        !           515: 
        !           516: <p>These caracters are:</p>
        !           517: 
        !           518: <ul>
        !           519:     <li>'c' to start again the program from the beginning.</li>
        !           520:     <li>'g' to made graphics. The output graphs are in GIF format
        !           521:         and you have no control over which is produced. If you
        !           522:         want to modify the graphics or make another one, you
        !           523:         should modify the parameters in the file <b>graph.gp</b>
        !           524:         located in imach\bin. A gnuplot reference manual is
        !           525:         available <a
        !           526:         href="http://www.cs.dartmouth.edu/gnuplot/gnuplot.html">here</a>.
        !           527:     </li>
        !           528:     <li>'e' opens the <strong>index.htm</strong> file to edit the
        !           529:         output files and graphs. </li>
        !           530:     <li>'q' for exiting.</li>
        !           531: </ul>
        !           532: 
        !           533: <h5><font size="4"><strong>Results files </strong></font><br>
        !           534: <br>
        !           535: <font color="#EC5E5E" size="3"><strong>- </strong></font><a
        !           536: name="Observed prevalence in each state"><font color="#EC5E5E"
        !           537: size="3"><strong>Observed prevalence in each state</strong></font></a><font
        !           538: color="#EC5E5E" size="3"><strong> (and at first pass)</strong></font><b>:
        !           539: </b><a href="prbiaspar.txt"><b>prbiaspar.txt</b></a><br>
        !           540: </h5>
        !           541: 
        !           542: <p>The first line is the title and displays each field of the
        !           543: file. The first column is age. The fields 2 and 6 are the
        !           544: proportion of individuals in states 1 and 2 respectively as
        !           545: observed during the first exam. Others fields are the numbers of
        !           546: people in states 1, 2 or more. The number of columns increases if
        !           547: the number of states is higher than 2.<br>
        !           548: The header of the file is </p>
        !           549: 
        !           550: <pre># Age Prev(1) N(1) N Age Prev(2) N(2) N
        !           551: 70 1.00000 631 631 70 0.00000 0 631
        !           552: 71 0.99681 625 627 71 0.00319 2 627 
        !           553: 72 0.97125 1115 1148 72 0.02875 33 1148 </pre>
        !           554: 
        !           555: <pre># Age Prev(1) N(1) N Age Prev(2) N(2) N
        !           556:     70 0.95721 604 631 70 0.04279 27 631</pre>
        !           557: 
        !           558: <p>It means that at age 70, the prevalence in state 1 is 1.000
        !           559: and in state 2 is 0.00 . At age 71 the number of individuals in
        !           560: state 1 is 625 and in state 2 is 2, hence the total number of
        !           561: people aged 71 is 625+2=627. <br>
        !           562: </p>
        !           563: 
        !           564: <h5><font color="#EC5E5E" size="3"><b>- Estimated parameters and
        !           565: covariance matrix</b></font><b>: </b><a href="rbiaspar.txt"><b>rbiaspar.txt</b></a></h5>
        !           566: 
        !           567: <p>This file contains all the maximisation results: </p>
        !           568: 
        !           569: <pre> Number of iterations=47
        !           570:  -2 log likelihood=46553.005854373667  
        !           571:  Estimated parameters: a12 = -12.691743 b12 = 0.095819 
        !           572:                        a13 = -7.815392   b13 = 0.031851 
        !           573:                        a21 = -1.809895 b21 = -0.030470 
        !           574:                        a23 = -7.838248  b23 = 0.039490  
        !           575:  Covariance matrix: Var(a12) = 1.03611e-001
        !           576:                     Var(b12) = 1.51173e-005
        !           577:                     Var(a13) = 1.08952e-001
        !           578:                     Var(b13) = 1.68520e-005  
        !           579:                     Var(a21) = 4.82801e-001
        !           580:                     Var(b21) = 6.86392e-005
        !           581:                     Var(a23) = 2.27587e-001
        !           582:                     Var(b23) = 3.04465e-005 
        !           583:  </pre>
        !           584: 
        !           585: <h5><font color="#EC5E5E" size="3"><b>- Transition probabilities</b></font><b>:
        !           586: </b><a href="pijrbiaspar.txt"><b>pijrbiaspar.txt</b></a></h5>
        !           587: 
        !           588: <p>Here are the transitions probabilities Pij(x, x+nh) where nh
        !           589: is a multiple of 2 years. The first column is the starting age x
        !           590: (from age 50 to 100), the second is age (x+nh) and the others are
        !           591: the transition probabilities p11, p12, p13, p21, p22, p23. For
        !           592: example, line 5 of the file is: </p>
        !           593: 
        !           594: <pre> 100 106 0.03286 0.23512 0.73202 0.02330 0.19210 0.78460 </pre>
        !           595: 
        !           596: <p>and this means: </p>
        !           597: 
        !           598: <pre>p11(100,106)=0.03286
        !           599: p12(100,106)=0.23512
        !           600: p13(100,106)=0.73202
        !           601: p21(100,106)=0.02330
        !           602: p22(100,106)=0.19210 
        !           603: p22(100,106)=0.78460 </pre>
        !           604: 
        !           605: <h5><font color="#EC5E5E" size="3"><b>- </b></font><a
        !           606: name="Stationary prevalence in each state"><font color="#EC5E5E"
        !           607: size="3"><b>Stationary prevalence in each state</b></font></a><b>:
        !           608: </b><a href="plrbiaspar.txt"><b>plrbiaspar.txt</b></a></h5>
        !           609: 
        !           610: <pre>#Age 1-1 2-2 
        !           611: 70 0.92274 0.07726 
        !           612: 71 0.91420 0.08580 
        !           613: 72 0.90481 0.09519 
        !           614: 73 0.89453 0.10547</pre>
        !           615: 
        !           616: <p>At age 70 the stationary prevalence is 0.92274 in state 1 and
        !           617: 0.07726 in state 2. This stationary prevalence differs from
        !           618: observed prevalence. Here is the point. The observed prevalence
        !           619: at age 70 results from the incidence of disability, incidence of
        !           620: recovery and mortality which occurred in the past of the cohort.
        !           621: Stationary prevalence results from a simulation with actual
        !           622: incidences and mortality (estimated from this cross-longitudinal
        !           623: survey). It is the best predictive value of the prevalence in the
        !           624: future if &quot;nothing changes in the future&quot;. This is
        !           625: exactly what demographers do with a Life table. Life expectancy
        !           626: is the expected mean time to survive if observed mortality rates
        !           627: (incidence of mortality) &quot;remains constant&quot; in the
        !           628: future. </p>
        !           629: 
        !           630: <h5><font color="#EC5E5E" size="3"><b>- Standard deviation of
        !           631: stationary prevalence</b></font><b>: </b><a
        !           632: href="vplrbiaspar.txt"><b>vplrbiaspar.txt</b></a></h5>
        !           633: 
        !           634: <p>The stationary prevalence has to be compared with the observed
        !           635: prevalence by age. But both are statistical estimates and
        !           636: subjected to stochastic errors due to the size of the sample, the
        !           637: design of the survey, and, for the stationary prevalence to the
        !           638: model used and fitted. It is possible to compute the standard
        !           639: deviation of the stationary prevalence at each age.</p>
        !           640: 
        !           641: <h6><font color="#EC5E5E" size="3">Observed and stationary
        !           642: prevalence in state (2=disable) with the confident interval</font>:<b>
        !           643: vbiaspar2.gif</b></h6>
        !           644: 
        !           645: <p><br>
        !           646: This graph exhibits the stationary prevalence in state (2) with
        !           647: the confidence interval in red. The green curve is the observed
        !           648: prevalence (or proportion of individuals in state (2)). Without
        !           649: discussing the results (it is not the purpose here), we observe
        !           650: that the green curve is rather below the stationary prevalence.
        !           651: It suggests an increase of the disability prevalence in the
        !           652: future.</p>
        !           653: 
        !           654: <p><img src="vbiaspar2.gif" width="400" height="300"></p>
        !           655: 
        !           656: <h6><font color="#EC5E5E" size="3"><b>Convergence to the
        !           657: stationary prevalence of disability</b></font><b>: pbiaspar1.gif</b><br>
        !           658: <img src="pbiaspar1.gif" width="400" height="300"> </h6>
        !           659: 
        !           660: <p>This graph plots the conditional transition probabilities from
        !           661: an initial state (1=healthy in red at the bottom, or 2=disable in
        !           662: green on top) at age <em>x </em>to the final state 2=disable<em> </em>at
        !           663: age <em>x+h. </em>Conditional means at the condition to be alive
        !           664: at age <em>x+h </em>which is <i>hP12x</i> + <em>hP22x</em>. The
        !           665: curves <i>hP12x/(hP12x</i> + <em>hP22x) </em>and <i>hP22x/(hP12x</i>
        !           666: + <em>hP22x) </em>converge with <em>h, </em>to the <em>stationary
        !           667: prevalence of disability</em>. In order to get the stationary
        !           668: prevalence at age 70 we should start the process at an earlier
        !           669: age, i.e.50. If the disability state is defined by severe
        !           670: disability criteria with only a few chance to recover, then the
        !           671: incidence of recovery is low and the time to convergence is
        !           672: probably longer. But we don't have experience yet.</p>
        !           673: 
        !           674: <h5><font color="#EC5E5E" size="3"><b>- Life expectancies by age
        !           675: and initial health status</b></font><b>: </b><a
        !           676: href="erbiaspar.txt"><b>erbiaspar.txt</b></a></h5>
        !           677: 
        !           678: <pre># Health expectancies 
        !           679: # Age 1-1 1-2 2-1 2-2 
        !           680: 70 10.7297 2.7809 6.3440 5.9813 
        !           681: 71 10.3078 2.8233 5.9295 5.9959 
        !           682: 72 9.8927 2.8643 5.5305 6.0033 
        !           683: 73 9.4848 2.9036 5.1474 6.0035 </pre>
        !           684: 
        !           685: <pre>For example 70 10.7297 2.7809 6.3440 5.9813 means:
        !           686: e11=10.7297 e12=2.7809 e21=6.3440 e22=5.9813</pre>
        !           687: 
        !           688: <pre><img src="exbiaspar1.gif" width="400" height="300"><img
        !           689: src="exbiaspar2.gif" width="400" height="300"></pre>
        !           690: 
        !           691: <p>For example, life expectancy of a healthy individual at age 70
        !           692: is 10.73 in the healthy state and 2.78 in the disability state
        !           693: (=13.51 years). If he was disable at age 70, his life expectancy
        !           694: will be shorter, 6.34 in the healthy state and 5.98 in the
        !           695: disability state (=12.32 years). The total life expectancy is a
        !           696: weighted mean of both, 13.51 and 12.32; weight is the proportion
        !           697: of people disabled at age 70. In order to get a pure period index
        !           698: (i.e. based only on incidences) we use the <a
        !           699: href="#Stationary prevalence in each state">computed or
        !           700: stationary prevalence</a> at age 70 (i.e. computed from
        !           701: incidences at earlier ages) instead of the <a
        !           702: href="#Observed prevalence in each state">observed prevalence</a>
        !           703: (for example at first exam) (<a href="#Health expectancies">see
        !           704: below</a>).</p>
        !           705: 
        !           706: <h5><font color="#EC5E5E" size="3"><b>- Variances of life
        !           707: expectancies by age and initial health status</b></font><b>: </b><a
        !           708: href="vrbiaspar.txt"><b>vrbiaspar.txt</b></a></h5>
        !           709: 
        !           710: <p>For example, the covariances of life expectancies Cov(ei,ej)
        !           711: at age 50 are (line 3) </p>
        !           712: 
        !           713: <pre>   Cov(e1,e1)=0.4667  Cov(e1,e2)=0.0605=Cov(e2,e1)  Cov(e2,e2)=0.0183</pre>
        !           714: 
        !           715: <h5><font color="#EC5E5E" size="3"><b>- </b></font><a
        !           716: name="Health expectancies"><font color="#EC5E5E" size="3"><b>Health
        !           717: expectancies</b></font></a><font color="#EC5E5E" size="3"><b>
        !           718: with standard errors in parentheses</b></font><b>: </b><a
        !           719: href="trbiaspar.txt"><font face="Courier New"><b>trbiaspar.txt</b></font></a></h5>
        !           720: 
        !           721: <pre>#Total LEs with variances: e.. (std) e.1 (std) e.2 (std) </pre>
        !           722: 
        !           723: <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>
        !           724: 
        !           725: <p>Thus, at age 70 the total life expectancy, e..=13.42 years is
        !           726: the weighted mean of e1.=13.51 and e2.=12.32 by the stationary
        !           727: prevalence at age 70 which are 0.92274 in state 1 and 0.07726 in
        !           728: state 2, respectively (the sum is equal to one). e.1=10.39 is the
        !           729: Disability-free life expectancy at age 70 (it is again a weighted
        !           730: mean of e11 and e21). e.2=3.03 is also the life expectancy at age
        !           731: 70 to be spent in the disability state.</p>
        !           732: 
        !           733: <h6><font color="#EC5E5E" size="3"><b>Total life expectancy by
        !           734: age and health expectancies in states (1=healthy) and (2=disable)</b></font><b>:
        !           735: ebiaspar.gif</b></h6>
        !           736: 
        !           737: <p>This figure represents the health expectancies and the total
        !           738: life expectancy with the confident interval in dashed curve. </p>
        !           739: 
        !           740: <pre>        <img src="ebiaspar.gif" width="400" height="300"></pre>
        !           741: 
        !           742: <p>Standard deviations (obtained from the information matrix of
        !           743: the model) of these quantities are very useful.
        !           744: Cross-longitudinal surveys are costly and do not involve huge
        !           745: samples, generally a few thousands; therefore it is very
        !           746: important to have an idea of the standard deviation of our
        !           747: estimates. It has been a big challenge to compute the Health
        !           748: Expectancy standard deviations. Don't be confuse: life expectancy
        !           749: is, as any expected value, the mean of a distribution; but here
        !           750: we are not computing the standard deviation of the distribution,
        !           751: but the standard deviation of the estimate of the mean.</p>
        !           752: 
        !           753: <p>Our health expectancies estimates vary according to the sample
        !           754: size (and the standard deviations give confidence intervals of
        !           755: the estimate) but also according to the model fitted. Let us
        !           756: explain it in more details.</p>
        !           757: 
        !           758: <p>Choosing a model means ar least two kind of choices. First we
        !           759: have to decide the number of disability states. Second we have to
        !           760: design, within the logit model family, the model: variables,
        !           761: covariables, confonding factors etc. to be included.</p>
        !           762: 
        !           763: <p>More disability states we have, better is our demographical
        !           764: approach of the disability process, but smaller are the number of
        !           765: transitions between each state and higher is the noise in the
        !           766: measurement. We do not have enough experiments of the various
        !           767: models to summarize the advantages and disadvantages, but it is
        !           768: important to say that even if we had huge and unbiased samples,
        !           769: the total life expectancy computed from a cross-longitudinal
        !           770: survey, varies with the number of states. If we define only two
        !           771: states, alive or dead, we find the usual life expectancy where it
        !           772: is assumed that at each age, people are at the same risk to die.
        !           773: If we are differentiating the alive state into healthy and
        !           774: disable, and as the mortality from the disability state is higher
        !           775: than the mortality from the healthy state, we are introducing
        !           776: heterogeneity in the risk of dying. The total mortality at each
        !           777: age is the weighted mean of the mortality in each state by the
        !           778: prevalence in each state. Therefore if the proportion of people
        !           779: at each age and in each state is different from the stationary
        !           780: equilibrium, there is no reason to find the same total mortality
        !           781: at a particular age. Life expectancy, even if it is a very useful
        !           782: tool, has a very strong hypothesis of homogeneity of the
        !           783: population. Our main purpose is not to measure differential
        !           784: mortality but to measure the expected time in a healthy or
        !           785: disability state in order to maximise the former and minimize the
        !           786: latter. But the differential in mortality complexifies the
        !           787: measurement.</p>
        !           788: 
        !           789: <p>Incidences of disability or recovery are not affected by the
        !           790: number of states if these states are independant. But incidences
        !           791: estimates are dependant on the specification of the model. More
        !           792: covariates we added in the logit model better is the model, but
        !           793: some covariates are not well measured, some are confounding
        !           794: factors like in any statistical model. The procedure to &quot;fit
        !           795: the best model' is similar to logistic regression which itself is
        !           796: similar to regression analysis. We haven't yet been sofar because
        !           797: we also have a severe limitation which is the speed of the
        !           798: convergence. On a Pentium III, 500 MHz, even the simplest model,
        !           799: estimated by month on 8,000 people may take 4 hours to converge.
        !           800: Also, the program is not yet a statistical package, which permits
        !           801: a simple writing of the variables and the model to take into
        !           802: account in the maximisation. The actual program allows only to
        !           803: add simple variables without covariations, like age+sex but
        !           804: without age+sex+ age*sex . This can be done from the source code
        !           805: (you have to change three lines in the source code) but will
        !           806: never be general enough. But what is to remember, is that
        !           807: incidences or probability of change from one state to another is
        !           808: affected by the variables specified into the model.</p>
        !           809: 
        !           810: <p>Also, the age range of the people interviewed has a link with
        !           811: the age range of the life expectancy which can be estimated by
        !           812: extrapolation. If your sample ranges from age 70 to 95, you can
        !           813: clearly estimate a life expectancy at age 70 and trust your
        !           814: confidence interval which is mostly based on your sample size,
        !           815: but if you want to estimate the life expectancy at age 50, you
        !           816: should rely in your model, but fitting a logistic model on a age
        !           817: range of 70-95 and estimating probabilties of transition out of
        !           818: this age range, say at age 50 is very dangerous. At least you
        !           819: should remember that the confidence interval given by the
        !           820: standard deviation of the health expectancies, are under the
        !           821: strong assumption that your model is the 'true model', which is
        !           822: probably not the case.</p>
        !           823: 
        !           824: <h5><font color="#EC5E5E" size="3"><b>- Copy of the parameter
        !           825: file</b></font><b>: </b><a href="orbiaspar.txt"><b>orbiaspar.txt</b></a></h5>
        !           826: 
        !           827: <p>This copy of the parameter file can be useful to re-run the
        !           828: program while saving the old output files. </p>
        !           829: 
        !           830: <hr>
        !           831: 
        !           832: <h2><a name="example" </a><font color="#00006A">Trying an example</font></a></h2>
        !           833: 
        !           834: <p>Since you know how to run the program, it is time to test it
        !           835: on your own computer. Try for example on a parameter file named <a
        !           836: href="file://../mytry/imachpar.txt">imachpar.txt</a> which is a
        !           837: copy of <font size="2" face="Courier New">mypar.txt</font>
        !           838: included in the subdirectory of imach, <font size="2"
        !           839: face="Courier New">mytry</font>. Edit it to change the name of
        !           840: the data file to <font size="2" face="Courier New">..\data\mydata.txt</font>
        !           841: if you don't want to copy it on the same directory. The file <font
        !           842: face="Courier New">mydata.txt</font> is a smaller file of 3,000
        !           843: people but still with 4 waves. </p>
        !           844: 
        !           845: <p>Click on the imach.exe icon to open a window. Answer to the
        !           846: question:'<strong>Enter the parameter file name:'</strong></p>
        !           847: 
        !           848: <table border="1">
        !           849:     <tr>
        !           850:         <td width="100%"><strong>IMACH, Version 0.63</strong><p><strong>Enter
        !           851:         the parameter file name: ..\mytry\imachpar.txt</strong></p>
        !           852:         </td>
        !           853:     </tr>
        !           854: </table>
        !           855: 
        !           856: <p>Most of the data files or image files generated, will use the
        !           857: 'imachpar' string into their name. The running time is about 2-3
        !           858: minutes on a Pentium III. If the execution worked correctly, the
        !           859: outputs files are created in the current directory, and should be
        !           860: the same as the mypar files initially included in the directory <font
        !           861: size="2" face="Courier New">mytry</font>.</p>
        !           862: 
        !           863: <ul>
        !           864:     <li><pre><u>Output on the screen</u> The output screen looks like <a
        !           865: href="imachrun.LOG">this Log file</a>
        !           866: #
        !           867: 
        !           868: title=MLE datafile=..\data\mydata.txt lastobs=3000 firstpass=1 lastpass=3
        !           869: ftol=1.000000e-008 stepm=24 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0</pre>
        !           870:     </li>
        !           871:     <li><pre>Total number of individuals= 2965, Agemin = 70.00, Agemax= 100.92
        !           872: 
        !           873: Warning, no any valid information for:126 line=126
        !           874: Warning, no any valid information for:2307 line=2307
        !           875: Delay (in months) between two waves Min=21 Max=51 Mean=24.495826
        !           876: <font face="Times New Roman">These lines give some warnings on the data file and also some raw statistics on frequencies of transitions.</font>
        !           877: Age 70 1.=230 loss[1]=3.5% 2.=16 loss[2]=12.5% 1.=222 prev[1]=94.1% 2.=14
        !           878:  prev[2]=5.9% 1-1=8 11=200 12=7 13=15 2-1=2 21=6 22=7 23=1
        !           879: Age 102 1.=0 loss[1]=NaNQ% 2.=0 loss[2]=NaNQ% 1.=0 prev[1]=NaNQ% 2.=0 </pre>
        !           880:     </li>
        !           881: </ul>
        !           882: 
        !           883: <p>&nbsp;</p>
        !           884: 
        !           885: <ul>
        !           886:     <li>Maximisation with the Powell algorithm. 8 directions are
        !           887:         given corresponding to the 8 parameters. this can be
        !           888:         rather long to get convergence.<br>
        !           889:         <font size="1" face="Courier New"><br>
        !           890:         Powell iter=1 -2*LL=11531.405658264877 1 0.000000000000 2
        !           891:         0.000000000000 3<br>
        !           892:         0.000000000000 4 0.000000000000 5 0.000000000000 6
        !           893:         0.000000000000 7 <br>
        !           894:         0.000000000000 8 0.000000000000<br>
        !           895:         1..........2.................3..........4.................5.........<br>
        !           896:         6................7........8...............<br>
        !           897:         Powell iter=23 -2*LL=6744.954108371555 1 -12.967632334283
        !           898:         <br>
        !           899:         2 0.135136681033 3 -7.402109728262 4 0.067844593326 <br>
        !           900:         5 -0.673601538129 6 -0.006615504377 7 -5.051341616718 <br>
        !           901:         8 0.051272038506<br>
        !           902:         1..............2...........3..............4...........<br>
        !           903:         5..........6................7...........8.........<br>
        !           904:         #Number of iterations = 23, -2 Log likelihood =
        !           905:         6744.954042573691<br>
        !           906:         # Parameters<br>
        !           907:         12 -12.966061 0.135117 <br>
        !           908:         13 -7.401109 0.067831 <br>
        !           909:         21 -0.672648 -0.006627 <br>
        !           910:         23 -5.051297 0.051271 </font><br>
        !           911:         </li>
        !           912:     <li><pre><font size="2">Calculation of the hessian matrix. Wait...
        !           913: 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
        !           914: 
        !           915: Inverting the hessian to get the covariance matrix. Wait...
        !           916: 
        !           917: #Hessian matrix#
        !           918: 3.344e+002 2.708e+004 -4.586e+001 -3.806e+003 -1.577e+000 -1.313e+002 3.914e-001 3.166e+001 
        !           919: 2.708e+004 2.204e+006 -3.805e+003 -3.174e+005 -1.303e+002 -1.091e+004 2.967e+001 2.399e+003 
        !           920: -4.586e+001 -3.805e+003 4.044e+002 3.197e+004 2.431e-002 1.995e+000 1.783e-001 1.486e+001 
        !           921: -3.806e+003 -3.174e+005 3.197e+004 2.541e+006 2.436e+000 2.051e+002 1.483e+001 1.244e+003 
        !           922: -1.577e+000 -1.303e+002 2.431e-002 2.436e+000 1.093e+002 8.979e+003 -3.402e+001 -2.843e+003 
        !           923: -1.313e+002 -1.091e+004 1.995e+000 2.051e+002 8.979e+003 7.420e+005 -2.842e+003 -2.388e+005 
        !           924: 3.914e-001 2.967e+001 1.783e-001 1.483e+001 -3.402e+001 -2.842e+003 1.494e+002 1.251e+004 
        !           925: 3.166e+001 2.399e+003 1.486e+001 1.244e+003 -2.843e+003 -2.388e+005 1.251e+004 1.053e+006 
        !           926: # Scales
        !           927: 12 1.00000e-004 1.00000e-006
        !           928: 13 1.00000e-004 1.00000e-006
        !           929: 21 1.00000e-003 1.00000e-005
        !           930: 23 1.00000e-004 1.00000e-005
        !           931: # Covariance
        !           932:   1 5.90661e-001
        !           933:   2 -7.26732e-003 8.98810e-005
        !           934:   3 8.80177e-002 -1.12706e-003 5.15824e-001
        !           935:   4 -1.13082e-003 1.45267e-005 -6.50070e-003 8.23270e-005
        !           936:   5 9.31265e-003 -1.16106e-004 6.00210e-004 -8.04151e-006 1.75753e+000
        !           937:   6 -1.15664e-004 1.44850e-006 -7.79995e-006 1.04770e-007 -2.12929e-002 2.59422e-004
        !           938:   7 1.35103e-003 -1.75392e-005 -6.38237e-004 7.85424e-006 4.02601e-001 -4.86776e-003 1.32682e+000
        !           939:   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
        !           940: # agemin agemax for lifexpectancy, bage fage (if mle==0 ie no data nor Max likelihood).
        !           941: 
        !           942: 
        !           943: agemin=70 agemax=100 bage=50 fage=100
        !           944: Computing prevalence limit: result on file 'plrmypar.txt' 
        !           945: Computing pij: result on file 'pijrmypar.txt' 
        !           946: Computing Health Expectancies: result on file 'ermypar.txt' 
        !           947: Computing Variance-covariance of DFLEs: file 'vrmypar.txt' 
        !           948: Computing Total LEs with variances: file 'trmypar.txt' 
        !           949: Computing Variance-covariance of Prevalence limit: file 'vplrmypar.txt' 
        !           950: End of Imach
        !           951: </font></pre>
        !           952:     </li>
        !           953: </ul>
        !           954: 
        !           955: <p><font size="3">Once the running is finished, the program
        !           956: requires a caracter:</font></p>
        !           957: 
        !           958: <table border="1">
        !           959:     <tr>
        !           960:         <td width="100%"><strong>Type g for plotting (available
        !           961:         if mle=1), e to edit output files, c to start again,</strong><p><strong>and
        !           962:         q for exiting:</strong></p>
        !           963:         </td>
        !           964:     </tr>
        !           965: </table>
        !           966: 
        !           967: <p><font size="3">First you should enter <strong>g</strong> to
        !           968: make the figures and then you can edit all the results by typing <strong>e</strong>.
        !           969: </font></p>
        !           970: 
        !           971: <ul>
        !           972:     <li><u>Outputs files</u> <br>
        !           973:         - index.htm, this file is the master file on which you
        !           974:         should click first.<br>
        !           975:         - Observed prevalence in each state: <a
        !           976:         href="..\mytry\prmypar.txt">mypar.txt</a> <br>
        !           977:         - Estimated parameters and the covariance matrix: <a
        !           978:         href="..\mytry\rmypar.txt">rmypar.txt</a> <br>
        !           979:         - Stationary prevalence in each state: <a
        !           980:         href="..\mytry\plrmypar.txt">plrmypar.txt</a> <br>
        !           981:         - Transition probabilities: <a
        !           982:         href="..\mytry\pijrmypar.txt">pijrmypar.txt</a> <br>
        !           983:         - Copy of the parameter file: <a
        !           984:         href="..\mytry\ormypar.txt">ormypar.txt</a> <br>
        !           985:         - Life expectancies by age and initial health status: <a
        !           986:         href="..\mytry\ermypar.txt">ermypar.txt</a> <br>
        !           987:         - Variances of life expectancies by age and initial
        !           988:         health status: <a href="..\mytry\vrmypar.txt">vrmypar.txt</a>
        !           989:         <br>
        !           990:         - Health expectancies with their variances: <a
        !           991:         href="..\mytry\trmypar.txt">trmypar.txt</a> <br>
        !           992:         - Standard deviation of stationary prevalence: <a
        !           993:         href="..\mytry\vplrmypar.txt">vplrmypar.txt</a> <br>
        !           994:         <br>
        !           995:         </li>
        !           996:     <li><u>Graphs</u> <br>
        !           997:         <br>
        !           998:         -<a href="..\mytry\vmypar1.gif">Observed and stationary
        !           999:         prevalence in state (1) with the confident interval</a> <br>
        !          1000:         -<a href="..\mytry\vmypar2.gif">Observed and stationary
        !          1001:         prevalence in state (2) with the confident interval</a> <br>
        !          1002:         -<a href="..\mytry\exmypar1.gif">Health life expectancies
        !          1003:         by age and initial health state (1)</a> <br>
        !          1004:         -<a href="..\mytry\exmypar2.gif">Health life expectancies
        !          1005:         by age and initial health state (2)</a> <br>
        !          1006:         -<a href="..\mytry\emypar.gif">Total life expectancy by
        !          1007:         age and health expectancies in states (1) and (2).</a> </li>
        !          1008: </ul>
        !          1009: 
        !          1010: <p>This software have been partly granted by <a
        !          1011: href="http://euroreves.ined.fr">Euro-REVES</a>, a concerted
        !          1012: action from the European Union. It will be copyrighted
        !          1013: identically to a GNU software product, i.e. program and software
        !          1014: can be distributed freely for non commercial use. Sources are not
        !          1015: widely distributed today. You can get them by asking us with a
        !          1016: simple justification (name, email, institute) <a
        !          1017: href="mailto:brouard@ined.fr">mailto:brouard@ined.fr</a> and <a
        !          1018: href="mailto:lievre@ined.fr">mailto:lievre@ined.fr</a> .</p>
        !          1019: 
        !          1020: <p>Latest version (0.63 of 16 march 2000) can be accessed at <a
        !          1021: href="http://euroeves.ined.fr/imach">http://euroreves.ined.fr/imach</a><br>
        !          1022: </p>
        !          1023: </body>
        !          1024: </html>

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