Literature DB >> 27487842

An extension of generalized pairwise comparisons for prioritized outcomes in the presence of censoring.

Julien Péron1,2, Marc Buyse3,4, Brice Ozenne1,2, Laurent Roche1,2, Pascal Roy1,2.   

Abstract

Generalized pairwise comparisons have been proposed to permit a comprehensive assessment of several prioritized outcomes between two groups of observations. This procedure estimates Δ, the net chance of a better outcome with treatment than with control by comparing the patients outcomes among all possible pairs taking one patient from the treatment group and one patient from the control group. For time to event outcomes, the standard procedure of generalized pairwise comparisons is analogous to the Gehan's modification of the Mann-Whitney test which is biased in presence of censored observation and less powerful than Efron's modification of this test. We adapt Efron's modification to generalized pairwise comparisons. We show how a pairwise contribution to Δ can be calculated from the estimates of the survival function in the presence of right-censored data. We performed a simulation study to assess the bias, the type I error and the power of the new procedure. The estimate of Δ with the new procedure is only slightly biased even in presence of heavy censoring. We also show how this bias can be corrected when only one time-to-event outcome is analyzed. The new procedure has higher power in most cases compared to the standard procedure.

Entities:  

Keywords:  Pairwise comparisons; multivariate analysis; randomized trial; survival outcome

Mesh:

Year:  2016        PMID: 27487842     DOI: 10.1177/0962280216658320

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  7 in total

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3.  Group sequential methods for the Mann-Whitney parameter.

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5.  Introducing a new estimator and test for the weighted all-cause hazard ratio.

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6.  Weighted composite time to event endpoints with recurrent events: comparison of three analytical approaches.

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Authors:  Mamoru Takenaka; Makoto Hosono; Madan M Rehani; Yasutaka Chiba; Rei Ishikawa; Ayana Okamoto; Tomohiro Yamazaki; Atsushi Nakai; Shunsuke Omoto; Kosuke Minaga; Ken Kamata; Kentaro Yamao; Shiro Hayashi; Tsutomu Nishida; Masatoshi Kudo
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  7 in total

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