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

["Joseph Coveney" <jcoveney@bigplanet.com>]

Shoryoku Hino wrote:

Thank you for your reply and kind advice. I should have made my question
clearer.
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 ( www.epi.msu.edu/janthony/requests/propensity/Hill_Commentary_2.pdf ); 
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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