{smcl}
{* 13feb2003}{...}
{hline}
help for {hi:stcompet}
{hline}

{title:Generate Cumulative Incidence in presence of Competing Events}

{p 4 13}{cmd:stcompet} {it:newvar} {cmd:=} {c -(} {cmd:ci} | {cmd:se} | {cmd:hi} | {cmd:lo}
[[{it:newvar} {cmd:=} {it:...}] [{it:...}] ] [{cmd:if} {it:exp}] [{cmd:in} {it:range}] {cmd:,}
{cmd:compet1(}{it:numlist}{cmd:)} [{cmd:compet2(}{it:numlist}{cmd:)} {cmd:compet3(}{it:numlist}{cmd:)}
{cmd:compet4(}{it:numlist}{cmd:)} {cmd:compet5(}{it:numlist}{cmd:)} {cmd:compet6(}{it:numlist}{cmd:)}
{cmd:by(}{it:varname}{cmd:)} {cmdab:l:evel}{cmd:(}{it:#}{cmd:)} ]



{p}{cmd:stcompet} is for use with survival-time data; see help {help st}. You must 
have {cmd:stset} your data before using this command; see help {help stset}.{p_end}
{p}In previous {cmd:stset} you must specify {cmdab:f:ailure(}{it:varname}{cmd:==}{it:numlist}
{cmd:)} where {it:numlist} refers to the event of interest.


Examples:

{p 4 4}Generate variables containing Cumulative Incidence and Standard Error{p_end}
{p 12 20}{inp:. stset survtime, f(event==1)}{p_end}
{p 12 20}{inp:. stcompet CumInc = ci SError = se, compet1(2) compet2(4)}{p_end}

{p 4 4}Generate variables containing Cumulative Incidence Confidence Bounds{p_end}
{p 12 20}{inp:. stcompet High = hi Low = lo, compet1(2) compet2(4)}{p_end}

{p 4 4}Note that each created variable contains functions for all competing events : i.e.
event of interest and events in compet# options. So if you want graph functions relating to each event
you need to type:{p_end} 
{p 12 20}{inp:. gen CumInc1 = CumInc if event==1}{p_end}
{p 12 20}{inp:. gen CumInc2 = CumInc if event==2}


{title:Description}

{p}In survival or cohort studies the failure of an individual may be one of several distinct 
failure types. In such a situation we observe an event of interest and one or more competing
events whose occurrence precludes or alters the probability of occurence of the first one.
{cmd:stcompet} creates variables containing Cumulative Incidence, a function that in this case
appropriately estimates the probability of occurrence of each endpoint, corresponding Standard Error
and Confidence Bounds.{p_end}
{p}The values in {it:numlist} of the previous {cmd:stset} are assumed as occurrence of event of interest.
In {cmd:compet#()} options you can specify {it:numlist} relating to the occurrence of up to six 
competing events.{p_end}


{title:Options}

{p 0 4}{cmd:compet1(}{it:numlist}{cmd:)} is not an option because at least one event must
to compete with the event of interest. A failure of a competing event occurs whenever {it:failvar} 
specified in previous {cmd:stset } takes on any of the values of this {it:numlist}. 

{p 0 4}{cmd:compet2(}{it:numlist}{cmd:)}{it:...}{cmd:compet6(}{it:numlist}{cmd:)} 
are optional. Each {it:numlist} refers to a failure for other competing events.

{p 0 4}{cmd:by(}{it:varname}{cmd:)} produces separate functions by making
separate calculations for each group identified by equal values of the
{cmd:by()} variable taking on integer or string values.

{p 0 4}{cmd:level(}{it:#}{cmd:)} specifies the confidence level, in percent,
for the pointwise confidence interval around the cumulative incidence functions; 
see help {help level}.



{title:Remarks}

{p}Cumulative Incidence is estimated by summing up to {it:t} {space 2} S{it:(t-1)} * h'{it:(t)},
{space 2} where S{it:(t-1)} is the KME of the overall survival function and h'{it:(t)} is 
the cause-specific hazard at the time {space 1}{it:t}

{p}Standard errors are computed according to the formula in Marubini & Valsecchi
(1995) p. 341. They derive the estimator using delta method.{p_end}
{p}Applying delta method Choudhury obtains an other formula presented as
Dinse and Larson's variance estimator of the Cumulative Incidence. He provides also
Splus codes to compute it. In my checks, using these codes in Splus, standard errors 
are exactly the same as computed using Marubini & Valsecchi's formula in Stata.

{p}Choudhury proposed and showed that log(-log) trasformation improve coverage accuracy
of the confidence intervals. So they are estimated using formula 4 of his article.


{title:Also see}

{p 1 10}Manual:  {hi:{bind:[R] st sts}}, {hi:{bind:[R] st sts generate}},
{hi:{bind:[R] st sts graph}}{p_end}
{p 0 19}On-line:  help for {help sts}, {help stset}{p_end}


{title:References}

{p 0 4} 1 - Marubini E., Valsecchi M.G. Analysing Survival Data from Clinical Trials and 
Observational Studies. Wiley: Chichester, 1995.

{p 0 4} 2 - Choudhury J.B. Non-parametric confidence interval estimation for competing risks 
analysis: application to contraceptive data. Statistics in Medicine 2002; 21: 1129-1144.

{p 0 4} 3 - Gooley T.A., Leisenring W. Crowley J. Storer B.E. Estimation of failure probabilities
in the presence of competing risks: new representations of old estimators. Statistics in 
Medicine 1999; 18: 695-706.


{title:Author}

        Enzo Coviello, Azienda U.S.L. BA/1, Italy
        coviello@mythnet.it
        