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Re: st: Re: statistical test and sensitivity analysis for matched pairs with censoring

From   Steven Samuels <>
Subject   Re: st: Re: statistical test and sensitivity analysis for matched pairs with censoring
Date   Tue, 28 Dec 2010 11:29:13 -0500

Shoryoku Hino

Also, try -stcox- with pair-strata. See page 330 of Prentice and Kalbfleisch, 2002, The Statistical Analysis of Failure Time Data, 2nd Ed, Wiley, NY. Adding the -frailty- option is not necessary, but adding -vce(cluster pair_id)- would be worthwhile. (The vce(cluster) option is not available in -sts test-.)

You can also do IPTW (ATT etc.) with propensity scores in -stcox- by setting up the data with probability weights via -svyset- (as well as - stset-). In that case it is immaterial whether you specify the pair id as a PSU in -svyset- or in the -vce(cluster pair_id)- option.

Compared to what has been proposed in the literature, the choices in Stata are not great. Many references can be found in Section 5 of Shih, Joanna and Michael Fay (2003), ‘Chapter 8: A Class of Permutation Tests for Some Two-Sample Survival Data Problems,’, in Geller, Nancy L. (ed.), Advances in Clinical Trial Biostatistics, CRC Press).

Note that 1:1 matching is not an optimal design, especially for survival data. N:M matching would be more informative in general, but with survival data, there's another reason to do it: a stratum with all members censored contributes nothing to the analysis, and the risk of this is greatest when n=2.


Steven J. Samuels
18 Cantine's Island
Saugerties NY 12477
Voice: 845-246-0774
Fax:    206-202-4783

On Dec 28, 2010, at 9:27 AM, Joseph Coveney wrote:

Shoryoku Hino wrote:

Thank you for your reply and kind advice. I should have made my question
All I want to know is the test for survival data in case of paired sample.

For example, if it were not survival data, we would use signed rank test for continuous variable in pared sample instead of unpaired t-test or rank sum test. I would use McNemar test for proportion instead of chi square test.
Rosenbaum's sensitivity test could be applicable in these cases.

In my case, survival data, I think there might be a better, more powerful
test for paired sample than log-rank test or Wilcoxon test.
I would like you to give me any suggestion.


There is a -strata()- option to -sts test- that would allow you to stratify on pairs. I don't know whether this achieves the same result as does what your
reference ("RF.Woolson") is talking about.

Have you considered modeling the survival time with -stcox- or -streg-
in lieu of paired-sample testing? The model would include some or all of the confounder covariates in addition to the propensity score or an indicator
variable for matched pair.  See, for example, A. Gelman & J. Hill, _Data
Analysis Using Regression and Multilevel/Hierarchical Models_ (New York:
Cambridge Univ. Press, 2007), pp. 206-12; J. Hill, Discussion of research
using propensity-score matching: Comments on 'A critical appraisal of
propensity-score matching in the medical literature between 1996 and 2003' by Peter Austin, Statistics in Medicine. _Statistics in Medicine_ 27:2055-61, 2008 ( );
and references cited in them.

Another alternative to a simple paired-sample test might be using the scores in
a weighted regression model, which would also include some or all of the
confounder covariates; see, for example, A. Nichols, Causal inference with observational data. _Stata Journal_ 7:507-41, 2007; A. Nichols, Erratum and discussion of propensity-score reweighting. _Stata Journal_ 8:532-39, 2008.

For Rosenbaum-bounds and related analysis, there are user-written Stata
commands -rbounds-, -sensatt- and -mhbounds-. The last has an associated
article (S. O. Becher & M. Caliendo, Sensitivity analysis for average
treatment effects. _Stata Journal_ 7:71-83, 2007) that refers to what had
been implemented in Stata up to then.  Again, I don't know of anything
specifically tailored to censored survival time as the outcome.

Joseph Coveney

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