help stpm2cif 
                                       also see:  stpm2  stpm2_postestimation  
-------------------------------------------------------------------------------

Title

    stpm2cif --  Competing risks post-estimation tool to estimate cumulative
                   incidence function after stpm2


Syntax

        stpm2cif newvarlist [, options]


    options                             Description
    -------------------------------------------------------------------------
    cause1...cause10                    
    obs                                  specifies the number of observations
                                          (of time) to predict for
    ci                                   calculates confidence intervals for
                                          cumulative incidence function
    mint                                 the minimum value of follow up time
    maxt                                 the maximum value of follow up time
    timename                             name of time variable derived by
                                          code
    hazard                               predicts cause-specific hazard
                                          function for each cause
    contmort                             predicts the contribution to total
                                          mortality
    conthaz                              predicts the contribution to hazard
    -------------------------------------------------------------------------


Description

    stpm2cif can be used after stpm2 to obtain cumulative incidence functions
    for up to 10 causes of death.


Options

    Note: in the table below, vn is an abbreviation for varname.

        +------+
    ----+ Main +-------------------------------------------------------------

    cause1(vn # [vn # ..])..cause10(vn # [vn # ..]) requests that the
        covariates specified by the listed varname(s) be set to # when
        predicting the cumulative incidence functions for each cause.  It is
        complusory to specify cause1 and cause2.

    obs(integer) specifies the number of observations (of time) to predict
        for (default 1000). Observations are evenly spread between the
        minimum and maximum value of follow-up time.

    ci calculates a 95% confidence interval for the cumulative incidence
        function and stores the confidence limits in CIF_newvar_lci and
        CIF_newvar_uci.

    mint(#) the minimum value of follow up time. The default is set as the
        minimum event time from stset.

    maxt(#) the maximum value of follow up time. The default is set as the
        maximum event time from stset.

    timename(newvarname) this is the time variable generated during
        predictions for the cumulative incidence function (default _newt).
        Note that this is the variable for time that needs to be used when
        plotting curves for the cumulative incidence function and the
        cause-specific hazard function.

    hazard predicts cause-specific hazard function for each cause.

    contmort predicts the contribution to total mortality.

    conthaz predicts the contribution to hazard.


Examples


This example is taken from a paper by Putter et al. (2007) where data were anal
> yzed on 324 homosexual men from the 
Amsterdam Cohort Studies on HIV infection and AIDS. For a more detailed descrip
> tion of the web based data see 
Example 4 in the following document http://www.stata.com/features/competing-ris
> ks/stcrreg.pdf.


Expand the data set so that each patient has a row of data for each of the two 
> competing events - time to SI 
phenotype appearance and diagnosis of AIDS. 

        webuse hiv_si
        gen id=_n
        expand 2


Create a cause variable variable for each of the two competing events. Generate
>  an event indicator by setting 
the cause variable equal to the status variable. The status variable indicates 
> whether the patient has experienced
either of the two events. Once this has been generated use it to stset the data
> .


        bysort id: gen cause= _n
        gen si=cause==1
        gen aids=cause==2
        gen event=(cause==status)
        stset time, failure(event)
 


Run stpm2 including each of the two competing events in the model.


        stpm2 si aids, scale(hazard) tvc(si aids) nocons rcsbaseoff dftvc(3)



Run stpm2cif to obtain the cumulative incidence function for each cause. Note t
> hat the names in the newvarlist 
are in capital letters so as not to overwrite the original variables with those
>  names.


        stpm2cif SI AIDS, cause1(si 1) cause2(aids 1)


A plot of the two cumulative incidence functions for SI appearance and diagnosi
> s of AIDS can be obtained as follows:


        twoway (line CIF_SI _newt)(line CIF_AIDS _newt), xtitle(Years from
            HIV infection) ytitle(Cumulative Incidence Function) ylabel(,
            angle(0))

The variables CIF_SI and CIF_AIDS are the cumulative incidence functions genera
> ted by the stpm2cif command.
Note that the curve is plotted against _newt which is the new time variable gen
> erate by stpm2cif during the predictions.


In order to plot a stacked graph of the cumulative incidence functions you need
>  to sum up the two functions.


        gen tot1=CIF_SI
        gen tot2=CIF_SI+CIF_AIDS


Now the stacked cumulative incidence graph can be plotted as follows:


        twoway (area tot2 _newt)(area tot1 _newt), xtitle(Years from HIV
            infection) ytitle(Cumulative Incidence Function) ylabel(,
            angle(0))


Note that the legend shows tot1 which represents CIF_SI and tot2 which represen
> ts CIF_AIDS.

Also see

    Online:  [ST] stpm2 [ST] stpm2_postestimation;