# Scales (for hessian or gradient estimation) +
- If mle=1 you can enter zeros: +
- If mle=0 you must enter a covariance matrix (usually obtained from an earlier run).
- -# Scales (for hessian or gradient estimation) 12 0. 0. 13 0. 0. 21 0. 0. 23 0. 0.
This is an output if mle=1. But it can be used as an input to get the various output data files (Health expectancies, stationary prevalence etc.) and figures without -rerunning the rather long maximisation phase (mle=0).
- -Each line starts with indices "ijk" followed by the -covariances between aij and bij:
- +rerunning the rather long maximisation phase (mle=0).+Each line starts with indices "ijk" followed by the +covariances between aij and bij:
121 Var(a12)
122 Cov(b12,a12) Var(b12)
...
232 Cov(b23,a12) Cov(b23,b12) ... Var (b23)
-
- If mle=1 you can enter zeros. -
- -# Covariance matrix 121 0. 122 0. 0. @@ -468,12 +517,8 @@ covariances between aij and bij: 212 0. 0. 0. 0. 0. 0. 231 0. 0. 0. 0. 0. 0. 0. 232 0. 0. 0. 0. 0. 0. 0. 0.-
- If mle=0 you must enter a covariance matrix (usually
- obtained from an earlier run).
-
+ obtained from an earlier run).
Age range for calculation of stationary
@@ -481,19 +526,19 @@ prevalences and health expectanciesagemin=70 agemax=100 bage=50 fage=100
-Once we obtained the estimated parameters, the program is able
+
Once we obtained the estimated parameters, the program is able
to calculated stationary prevalence, transitions probabilities
and life expectancies at any age. Choice of age range is useful
for extrapolation. In our data file, ages varies from age 70 to
-102. Setting bage=50 and fage=100, makes the program computing
-life expectancy from age bage to age fage. As we use a model, we
-can compute life expectancy on a wider age range than the age
-range from the data. But the model can be rather wrong on big
-intervals.
-
-Similarly, it is possible to get extrapolated stationary
-prevalence by age ranging from agemin to agemax.
+102. It is possible to get extrapolated stationary prevalence by
+age ranging from agemin to agemax.
+
Setting bage=50 (begin age) and fage=100 (final age), makes
+the program computing life expectancy from age 'bage' to age
+'fage'. As we use a model, we can interessingly compute life
+expectancy on a wider age range than the age range from the data.
+But the model can be rather wrong on much larger intervals.
+Program is limited to around 120 for upper age!
- agemin= Minimum age for calculation of the
stationary prevalence
@@ -510,11 +555,10 @@ color="#FF0000"> the observed prevalence
begin-prev-date=1/1/1984 end-prev-date=1/6/1988
-Statements 'begin-prev-date' and 'end-prev-date' allow to
+
Statements 'begin-prev-date' and 'end-prev-date' allow to
select the period in which we calculate the observed prevalences
in each state. In this example, the prevalences are calculated on
-data survey collected between 1 january 1984 and 1 june 1988.
-
+data survey collected between 1 january 1984 and 1 june 1988.
- begin-prev-date= Starting date
(day/month/year)
@@ -527,30 +571,47 @@ expectancies
Once we obtained the estimated parameters, the program is able to calculated stationary prevalence, transitions probabilities and life expectancies at any age. Choice of age range is useful for extrapolation. In our data file, ages varies from age 70 to -102. Setting bage=50 and fage=100, makes the program computing -life expectancy from age bage to age fage. As we use a model, we -can compute life expectancy on a wider age range than the age -range from the data. But the model can be rather wrong on big -intervals.
Statements 'begin-prev-date' and 'end-prev-date' allow to select the period in which we calculate the observed prevalences in each state. In this example, the prevalences are calculated on -data survey collected between 1 january 1984 and 1 june 1988.
pop_based=0-
The user has the possibility to choose between -population-based or status-based health expectancies. If -pop_based=0 then status-based health expectancies are computed -and if pop_based=1, the programme computes population-based -health expectancies. Health expectancies are weighted averages of -health expectancies respective of the initial state. For a -status-based index, the weights are the cross-sectional -prevalences observed between two dates, as previously -explained, whereas for a population-based index, the weights -are the stationary prevalences.
+The program computes status-based health expectancies, i.e
+health expectancies which depends on your initial health state.
+If you are healthy your healthy life expectancy (e11) is higher
+than if you were disabled (e21, with e11 > e21).
+To compute a healthy life expectancy independant of the initial
+status we have to weight e11 and e21 according to the probability
+to be in each state at initial age or, with other word, according
+to the proportion of people in each state.
+We prefer computing a 'pure' period healthy life expectancy based
+only on the transtion forces. Then the weights are simply the
+stationnary prevalences or 'implied' prevalences at the initial
+age.
+Some other people would like to use the cross-sectional
+prevalences (the "Sullivan prevalences") observed at
+the initial age during a period of time defined
+just above.
+
+
-
+
- popbased= 0 Health expectancies are + computed at each age from stationary prevalences + 'expected' at this initial age. +
- popbased= 1 Health expectancies are + computed at each age from cross-sectional 'observed' + prevalence at this initial age. As all the population is + not observed at the same exact date we define a short + period were the observed prevalence is computed. +
Prevalence forecasting
+Prevalence forecasting ( Experimental)
starting-proj-date=1/1/1989 final-proj-date=1/1/1992 mov_average=0
Prevalence and population projections are only available if -the interpolation unit is a month, i.e. stepm=1. The programme -estimates the prevalence in each state at a precise date -expressed in day/month/year. The programme computes one -forecasted prevalence a year from a starting date (1 january of -1989 in this example) to a final date (1 january 1992). The -statement mov_average allows to compute smoothed forecasted -prevalences with a five-age moving average centered at the -mid-age of the five-age period.
+the interpolation unit is a month, i.e. stepm=1 and if there are +no covariate. The programme estimates the prevalence in each +state at a precise date expressed in day/month/year. The +programme computes one forecasted prevalence a year from a +starting date (1 january of 1989 in this example) to a final date +(1 january 1992). The statement mov_average allows to compute +smoothed forecasted prevalences with a five-age moving average +centered at the mid-age of the five-age period.- starting-proj-date= starting date
@@ -569,34 +630,55 @@ forecasting
popforecast=0 popfile=pyram.txt popfiledate=1/1/1989 last-popfiledate=1/1/1992
This command is available if the interpolation unit is a -month, i.e. stepm=1 and if popforecast=1. From a data file
- -Structure of the data file pyram.txt -: age numbers
- -
+month, i.e. stepm=1 and if popforecast=1. From a data file +including age and number of persons alive at the precise date +‘popfiledate’, you can forecast the number of persons +in each state until date ‘last-popfiledate’. In this +example, the popfile pyram.txt +includes real data which are the Japanese population in 1989.
+ +-
+
- popforecast= + 0 Option for population forecasting. If + popforecast=1, the programme does the forecasting. +
- popfile= + name of the population file +
- popfiledate= + date of the population population +
- last-popfiledate= + date of the last population projection +
Running Imach with this example
-We assume that you entered your 1st_example -parameter file as explained above. To -run the program you should click on the imach.exe icon and enter +We assume that you typed in your 1st_example +parameter file as explained above. +
To run the program you should either: +- click on the imach.exe icon and enter
the name of the parameter file which is for example C:\usr\imach\mle\biaspar.txt
-(you also can click on the biaspar.txt icon located in
-C:\usr\imach\mle and put it with -the mouse on the imach window).
- +href="C:\usr\imach\mle\biaspar.imach">C:\usr\imach\mle\biaspar.imach + - You also can locate the biaspar.imach icon in +C:\usr\imach\mle with your mouse and drag it with +the mouse on the imach window). +
- With latest version (0.7 and higher) if you setup windows in order to +understand ".imach" extension you can right click the +biaspar.imach icon and either edit with notepad the parameter file or +execute it with imach or whatever. +
The time to converge depends on the step unit that you used (1 +The time to converge depends on the step unit that you used (1 month is cpu consuming), on the number of cases, and on the -number of variables.
+number of variables. -The program outputs many files. Most of them are files which -will be plotted for better understanding.
+
The program outputs many files. Most of them are files which +will be plotted for better understanding.
@@ -608,9 +690,9 @@ with a grapher. We use Gnuplot which is program copyrighted but freely distributed. A gnuplot reference manual is available here.
When the running is finished, the user should enter a caracter -for plotting and output editing. +for plotting and output editing. -These caracters are:
+
These caracters are:
- 'c' to start again the program from the beginning. @@ -648,7 +730,7 @@ people aged 71 is 625+2=627.
- Estimated parameters and -covariance matrix: rbiaspar.txt
+covariance matrix: rbiaspar.imachThis file contains all the maximisation results:
@@ -775,18 +857,18 @@ href="erbiaspar.txt">erbiaspar.txt -For example 70 10.9226 3.0401 5.6488 6.2122 means: -e11=10.9226 e12=3.0401 e21=5.6488 e22=6.2122
+For example 70 10.4227 3.0402 5.6488 5.7123 means: +e11=10.4227 e12=3.0402 e21=5.6488 e22=5.7123
For example, life expectancy of a healthy individual at age 70 -is 10.92 in the healthy state and 3.04 in the disability state -(=13.96 years). If he was disable at age 70, his life expectancy -will be shorter, 5.64 in the healthy state and 6.21 in the -disability state (=11.85 years). The total life expectancy is a -weighted mean of both, 13.96 and 11.85; weight is the proportion +is 10.42 in the healthy state and 3.04 in the disability state +(=13.46 years). If he was disable at age 70, his life expectancy +will be shorter, 5.64 in the healthy state and 5.71 in the +disability state (=11.35 years). The total life expectancy is a +weighted mean of both, 13.46 and 11.35; weight is the proportion of people disabled at age 70. In order to get a pure period index (i.e. based only on incidences) we use the computed or @@ -813,14 +895,14 @@ href="trbiaspar.txt">-Total life expectancy by @@ -921,16 +1003,30 @@ program while saving the old output file
75 487781.02 91367.97 121915.51 74 512892.07 85003.47 117282.76 +- Prevalence forecasting: frbiaspar.txt
-On a d'abord estimé la date moyenne des interviaew. ie -13/9/1995. This file contains
- -Example, at date 1/1/1989 :
- -73 0.807 0.078 0.115
- -This means that at age 73, the probability for a person age 70 -at 13/9/1989 to be in state 1 is 0.807, in state 2 is 0.078 and -to die is 0.115 at 1/1/1989.
+First, +we have estimated the observed prevalence between 1/1/1984 and +1/6/1988. The mean date of interview (weighed average of the +interviews performed between1/1/1984 and 1/6/1988) is estimated +to be 13/9/1985, as written on the top on the file. Then we +forecast the probability to be in each state.
+ +Example, +at date 1/1/1989 :
+ +# StartingAge FinalAge P.1 P.2 P.3 +# Forecasting at date 1/1/1989 + 73 0.807 0.078 0.115
+ +Since +the minimum age is 70 on the 13/9/1985, the youngest forecasted +age is 73. This means that at age a person aged 70 at 13/9/1989 +has a probability to enter state1 of 0.807 at age 73 on 1/1/1989. +Similarly, the probability to be in state 2 is 0.078 and the +probability to die is 0.115. Then, on the 1/1/1989, the +prevalence of disability at age 73 is estimated to be 0.088.
- Population forecasting: poprbiaspar.txt
@@ -946,14 +1042,19 @@ to die is 0.115 at 1/1/1989.From the population file, we estimate the number of people in +each state. At age 73, 645857 persons are in state 1 and 69320 +are in state 2. One year latter, 512892 are still in state 1, +85003 are in state 2 and 117282 died before 1/1/1990.
+
-Trying an example
+Trying an example
Since you know how to run the program, it is time to test it on your own computer. Try for example on a parameter file named imachpar.txt which is a copy of mypar.txt included in the +href="..\mytry\imachpar.imach">imachpar.imach which is a copy of mypar.imach included in the subdirectory of imach, mytry. Edit it to change the name of the data file to ..\data\mydata.txt if you don't want to @@ -965,8 +1066,8 @@ question:'Enter the parameter fi
@@ -984,7 +1085,7 @@ href="imachrun.LOG">this Log file # title=MLE datafile=..\data\mydata.txt lastobs=3000 firstpass=1 lastpass=3 -ftol=1.000000e-008 stepm=24 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0 +ftol=1.000000e-008 stepm=24 ncovcol=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0- IMACH, Version 0.7 Enter - the parameter file name: ..\mytry\imachpar.txt
+IMACH, Version 0.8 Enter + the parameter file name: ..\mytry\imachpar.imach
Total number of individuals= 2965, Agemin = 70.00, Agemax= 100.92 @@ -1089,7 +1190,7 @@ edit the master file mypar.htm. < - Observed prevalence in each state: pmypar.txt
- Estimated parameters and the covariance matrix: rmypar.txt
+ href="..\mytry\rmypar.txt">rmypar.imach
- Stationary prevalence in each state: plrmypar.txt
- Transition probabilities: <- Graphs
- -One-step transition - probabilities
- -Convergence to the - stationary prevalence
- -Observed and stationary - prevalence in state (1) with the confident interval
- -Observed and stationary - prevalence in state (2) with the confident interval
- -Health life - expectancies by age and initial health state (1)
- -Health life - expectancies by age and initial health state (2)
- -Total life expectancy by - age and health expectancies in states (1) and (2).
+ -One-step transition probabilities
+ -Convergence to the stationary prevalence
+ -Observed and stationary prevalence in state (1) with the confident interval
+ -Observed and stationary prevalence in state (2) with the confident interval
+ -Health life expectancies by age and initial health state (1)
+ -Health life expectancies by age and initial health state (2)
+ -Total life expectancy by age and health expectancies in states (1) and (2).
This software have been partly granted by mailto:brouard@ined.fr and mailto:lievre@ined.fr .
-Latest version (0.7 of February 2002) can be accessed at http://euroreves.ined.fr/imach
+
Latest version (0.8 of March 2002) can be accessed at http://euroreves.ined.fr/imach