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Generalized pairwise comparisons using pseudo-observations for time-to-event censored data in a randomized controlled

Stephanie Pan1, Prasad Patil1, Janice Weinberg1

  • 1Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.

Statistical Methods in Medical Research
|January 19, 2026
PubMed
Summary

A new method using pseudo-observations improves generalized pairwise comparison (GPC) for censored time-to-event data in clinical trials. It reduces bias and error, especially with unequal dropout, enhancing treatment effect analysis.

Keywords:
Generalized pairwise comparisonscensored dataclinical trialspseudo-observationssurvival analysiswin ratio

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Survival Analysis

Background:

  • Generalized pairwise comparison (GPC) methods extend Mann-Whitney for prioritized outcome ranking in randomized controlled trials (RCTs).
  • GPC methods can analyze censored time-to-event data, but bias can arise from indeterminate pairs due to censoring.
  • The win ratio (WR) is a GPC measure interpretable as the inverse hazard ratio, but sensitive to censoring.

Purpose of the Study:

  • To propose a novel GPC method using pseudo-observations to address uninformative pairs caused by censoring in time-to-event outcomes.
  • To evaluate the performance of the proposed method against existing GPC approaches via simulations under diverse censoring scenarios.

Main Methods:

  • Development of a novel GPC approach utilizing pseudo-observations to handle censored data.
  • Comparative simulation studies assessing performance under various censoring proportions and distributions (equal vs. unequal dropout).
  • Evaluation of bias, root mean squared error, and statistical power against established GPC methods (Gehan and Latta).

Main Results:

  • The proposed method shows comparable performance to existing GPC methods under equal dropout and administrative censoring.
  • For unequal dropout, performance is contingent on censoring proportion and distribution, with the new method reducing bias and RMSE.
  • Statistical power improvements were not observed with the proposed method, despite reductions in bias and error.

Conclusions:

  • The novel pseudo-observation GPC method effectively mitigates bias and error associated with censoring in time-to-event data, particularly in scenarios with unequal dropout.
  • While not increasing statistical power, the method offers a valuable alternative for analyzing RCT data with censored outcomes.
  • The approach was successfully illustrated using reconstructed randomized controlled trial datasets.