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From |
"Shoryoku Hino" <shoryok@ninus.ocn.ne.jp> |

To |
<statalist@hsphsun2.harvard.edu> |

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

Date |
Sat, 1 Jan 2011 15:34:05 +0900 |

Joseph and Steve, Your suggestions are great help. Related to the tests for matched pair, I am going to make it with -sts test-or -stcox- using pair strata. I had actually considered IPTW methods as one of the other options. But as Joseph said, is there any tailored method of -rbounds- or -mhbounds- to survival censored data as outcome? Sho -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Steven Samuels Sent: Wednesday, December 29, 2010 1:29 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Re: statistical test and sensitivity analysis for matched pairs with censoring 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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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