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

[Steven Samuels <sjsamuels@gmail.com>]

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.

Steve

Steven J. Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
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
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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