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Re: st: weighted time dependent Cox model
From
Steve Samuels <[email protected]>
To
[email protected]
Subject
Re: st: weighted time dependent Cox model
Date
Sun, 19 Feb 2012 07:32:18 -0500
Correction: "exit() specifies the latest time under which the subject is both under observation and at risk of the failure event." (Stata 12 Survival Manual, page 486).
S.
In Stata, -enter- refers to the earliest entry time for a subject, and -exit- to the last observed time. (This is in the Manual entry for -stset-). Use this -stset- statement:
****************************************************
stset exit [pweight=weight], fail(event) id(id)
****************************************************'
Steve
[email protected]
On Feb 18, 2012, at 6:07 PM, Ehsan Karim wrote:
Dear Stata list,
I am trying to reproduce the weighted time dependent Cox model
(Andersen–Gill format with IPTW) results in Stata that are originally
obtained from R using same dataset, but so far getting the estimates
different. Could anyone indicate what I could be done to fix this?
Any suggestions/references will be highly appreciated.
Thanks,
Ehsan
##########################################
# R results: coef -0.288 se 0.174
##########################################
> dataset = read.csv("http://stat.ubc.ca/~e.karim/dataset.csv";)
> msmc = coxph(Surv(enter, exit, event) ~ tx + cluster(id), robust = TRUE, data = dataset, weights = weight)
> summary(msmc)
n= 14372, number of events= 131
coef exp(coef) se(coef) robust se z Pr(>|z|)
tx -0.2882 0.7496 0.1745 0.1964 -1.467 0.142
exp(coef) exp(-coef) lower .95 upper .95
tx 0.7496 1.334 0.5101 1.102
Concordance= 0.534 (se = 0.021 )
Rsquare= 0 (max possible= 0.136 )
Likelihood ratio test= 2.8 on 1 df, p=0.09455
Wald test = 2.15 on 1 df, p=0.1423
Score (logrank) test = 2.75 on 1 df, p=0.09739, Robust = 2.11 p=0.1459
##########################################
# Stata results: coef -.605 se .643
##########################################
. use http://stat.ubc.ca/~e.karim/dataset, clear
(6 vars, 14372 obs)
. stset exit [pweight=weight], fail(event) exit(exit) id(id) enter(enter)
id: id
failure event: event != 0 & event < .
obs. time interval: (exit[_n-1], exit]
enter on or after: time enter
exit on or before: time exit
weight: [pweight=weight]
------------------------------------------------------------------------------
14372 total obs.
12872 obs. begin on or after exit
------------------------------------------------------------------------------
1500 obs. remaining, representing
1500 subjects
17 failures in single failure-per-subject data
1500 total analysis time at risk, at risk from t = 0
earliest observed entry t = 0
last observed exit t = 1
. stcox tx, nohr robust nolog
failure _d: event
analysis time _t: exit
enter on or after: time enter
exit on or before: time exit
id: id
weight: [pweight=weight]
(sum of wgt is 1.5062e+03)
Cox regression -- Breslow method for ties
No. of subjects = 1506.227846 Number of obs = 1500
No. of failures = 17.16531456
Time at risk = 1506.227846
Wald chi2(1) = 0.89
Log pseudolikelihood = -124.48415 Prob > chi2 = 0.3464
(Std. Err. adjusted for 1500 clusters in id)
------------------------------------------------------------------------------
| Robust
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
tx | -.6053404 .6428669 -0.94 0.346 -1.865336 .6546555
------------------------------------------------------------------------------
##########################################
# dataset (partial)
##########################################
. list
+-------------------------------------------------------------------+
| id tx event enter exit weight _st _d _t _t0 |
|-------------------------------------------------------------------|
1. | 1 0 0 0 1 1.356058 1 0 1 0 |
2. | 1 0 0 1 2 1.356058 0 . . . |
3. | 1 0 0 2 3 1.356058 0 . . . |
4. | 1 1 0 3 4 1.356058 0 . . . |
5. | 1 0 0 4 5 1.356058 0 . . . |
6. | 1 0 0 5 6 1.356058 0 . . . |
7. | 1 0 0 6 7 1.356058 0 . . . |
8. | 1 0 0 7 8 1.356058 0 . . . |
9. | 1 0 0 8 9 1.356058 0 . . . |
10. | 1 1 0 9 10 1.356058 0 . . . |
|-------------------------------------------------------------------|
11. | 2 0 0 0 1 1.57168 1 0 1 0 |
12. | 2 0 0 1 2 1.57168 0 . . . |
13. | 2 1 0 2 3 1.57168 0 . . . |
14. | 2 0 0 3 4 1.57168 0 . . . |
15. | 2 1 0 4 5 1.57168 0 . . . |
16. | 2 1 0 5 6 1.57168 0 . . . |
17. | 2 1 0 6 7 1.57168 0 . . . |
18. | 2 0 0 7 8 1.57168 0 . . . |
19. | 2 0 0 8 9 1.57168 0 . . . |
20. | 2 0 0 9 10 1.57168 0 . . . |
|-------------------------------------------------------------------|
21. | 3 0 0 0 1 .983771 1 0 1 0 |
22. | 3 0 0 1 2 .983771 0 . . . |
23. | 3 0 0 2 3 .983771 0 . . . |
24. | 3 0 0 3 4 .983771 0 . . . |
25. | 3 1 0 4 5 .983771 0 . . . |
26. | 3 0 0 5 6 .983771 0 . . . |
27. | 3 0 0 6 7 .983771 0 . . . |
28. | 3 0 0 7 8 .983771 0 . . . |
29. | 3 1 0 8 9 .983771 0 . . . |
30. | 3 0 0 9 10 .983771 0 . . . |
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