help for stcompet

Generate Cumulative Incidence in presence of Competing Events

stcompet newvarname = { ci | se | hi | lo }
[[newvarname = ...] [...] ] [if exp] [in range] ,
compet1(numlist) [compet2(numlist) compet3(numlist)
compet4(numlist) compet5(numlist) compet6(numlist)
by(varname) allignol level(#) ]

stcompet is for use with survival-time data; see help st. You must
have stset your data before using this command; see help stset.

In previous stset you must specify failure(varname==numlist
) where numlist refers to the event of interest.

Examples:

Generate variables containing Cumulative Incidence and Standard Error

. stset survtime, f(event==1)

. stcompet CumInc = ci SError = se, compet1(2) compet2(4)

Generate variables containing Cumulative Incidence Confidence Bounds

. stcompet High = hi Low = lo, compet1(2) compet2(4)

Note that each created variable contains the function for all competing events : i.e. the
event of interest specified in stset statement and the events in compet# options. So if you want graph functions relating to each event
you need to type:

. gen CumInc1 = CumInc if event==1

. gen CumInc2 = CumInc if event==2

Description

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.
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.

The values in numlist of the previous stset are assumed as occurrence of event of interest.
In compet#() options you can specify numlist relating to the occurrence of up to six
competing events.

Functions

ci produces the cumulative incidence function.

se produces the standard error of the cumulative incidence.

hi produces the higher bound of the confidence interval based on
ln[-ln(Cumulative Incidence)].

lo produces the lower bound of the confidence interval based on
ln[-ln(Cumulative Incidence)].

Options

compet1(numlist) is not an option because at least one event must
compete with the event of interest. A failure of a competing event occurs whenever
failvar specified in the previous stset takes on any of the values of this
numlist. Note that the function required will be estimated relating to this
competing event and to the event of interest as well.

compet2(numlist)...compet6(numlist)
are optional. Each numlist refers to a failure for other competing events.

by(varname) produces separate functions by making
separate calculations for each group identified by equal values of the
by() variable taking on integer or string values.

allignol specifies that confidence bounds are computed
by applying log-log transformation to 1-cumulative incidence as suggested by Lin (1997) and Allignol et al.(2010).
Confidence bounds are then equal to those obtained via the etm package in R.

level(#) specifies the confidence level, in percent,
for the pointwise confidence interval around the cumulative incidence functions;
see help level.

Remarks

Cumulative Incidence is estimated by summing up to t S(t-1) * h'(t),
where S(t-1) is the KME of the overall survival function and h'(t) is
the cause-specific hazard at the time t.

Standard errors are computed according to the formula in Marubini & Valsecchi
(1995) p. 341. They derive the estimator using delta method.

stcompet may by used also with left-truncated survival data.

Also see

On-line: [ST] stset, [ST] sts gen, [ST] stcrreg

References

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

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

3 - Gooley T.A., Leisenring W., Crowley J., and 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.

4 - Lin D.Y. Non-parametric inference for cumulative incidence functions in competing risks studies.
Statistics in Medicine 1997; 16: 901-910.

5 - Allignol A., Schumacher M., and Beyersmann, J. A note on variance estimation of the Aalen-Johansen
estimator of the cumulative incidence function in competing risks, with a view toward left-truncated data.
Biometrical Journal 2010; 52(1): 126-137.

Authors

Enzo Coviello, Local Health Unit BT, Italy
enzo.coviello@tin.it

May Boggess