--- imach096d/doc/imach.htm 2002/03/10 15:54:47 1.7 +++ imach096d/doc/imach.htm 2002/03/11 15:24:05 1.9 @@ -1,4 +1,4 @@ - + @@ -7,6 +7,13 @@ content="text/html; charset=iso-8859-1"> Computing Health Expectancies using IMaCh + + + + + + @@ -320,9 +327,7 @@ line
  • ...
  • -
  • ncov=2 Number of covariates in the datafile. The - intercept and the age parameter are counting for 2 - covariates.
  • +
  • ncov=2 Number of covariates in the datafile.
  • nlstate=2 Number of non-absorbing (alive) states. Here we have two alive states: disability-free is coded 1 and disability is coded 2.
  • @@ -348,7 +353,7 @@ line

    Covariates

    Intercept and age are systematically included in the model. -Additional covariates (actually two) can be included with the command:

    +Additional covariates can be included with the command:

    model=list of covariates
    @@ -368,6 +373,19 @@ Additional covariates (actually two) can the product covariate*age +

    In this example, we have two covariates in the data file +(fields 2 and 3). The number of covariates is defined with +statement ncov=2. If now you have 3 covariates in the datafile +(fields 2, 3 and 4), you have to set ncov=3. Then you can run the +programme with a new parametrisation taking into account the +third covariate. For example, model=V1+V3 estimates +a model with the first and third covariates. More complicated +models can be used, but it will takes more time to converge. With +a simple model (no covariates), the programme estimates 8 +parameters. Adding covariates increases the number of parameters +: 12 for model=V1, 16 for model=V1+V1*age +and 20 for model=V1+V2+V3.

    +

    Guess values for optimization

    @@ -397,7 +415,8 @@ aij bij

    23 -6.234642 0.022315 -

    or, to simplify (in most of cases it converges but there is no warranty!):

    +

    or, to simplify (in most of cases it converges but there is no +warranty!):

    12 0.0 0.0
    @@ -406,6 +425,14 @@ aij bij 

    23 0.0 0.0
    +

    In order to speed up the convergence you can make a first run with +a large stepm i.e stepm=12 or 24 and then decrease the stepm until +stepm=1 month. If newstepm is the new shorter stepm and stepm can be +expressed as a multiple of newstepm, like newstepm=n stepm, then the +following approximation holds: +

    aij(n stepm) = aij(stepm) +ln(n)
    +
    and +
    bij(n stepm) = bij(stepm) .

    Guess values for computing variances

    This is an output if mle=1. But it can be @@ -484,15 +511,15 @@ prevalences and health expectancies - +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!

    +

    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!