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Generalized pairwise comparisons for censored data: An overview.

Vaiva Deltuvaite-Thomas1, Johan Verbeeck2, Tomasz Burzykowski1,2

  • 1International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium.

Biometrical Journal. Biometrische Zeitschrift
|September 20, 2022
PubMed
Summary
This summary is machine-generated.

Generalized pairwise comparisons (GPC) methods effectively analyze censored data. Methods ignoring uninformative pairs offer comparable power, especially with high censoring rates, making them suitable for survival data analysis.

Keywords:
biascensored outcomegeneralized pairwise comparisonsnet benefitstatistical power

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Nonparametric Statistics

Background:

  • The Wilcoxon-Mann-Whitney test is a standard for comparing two groups.
  • Censored data presents challenges in statistical analysis due to missing information.
  • Various generalized pairwise comparison (GPC) methods exist to address censored data.

Purpose of the Study:

  • To evaluate generalized pairwise comparison (GPC) methods for censored data.
  • To compare GPC methods in hypothesis testing and estimation of treatment effects.
  • To provide recommendations for selecting appropriate GPC methods based on censoring levels.

Main Methods:

  • Review and comparison of GPC methods, including those ignoring noninformative pairs (Gehan, Harrell, Buyse), imputation-based methods (Efron, Peron, Latta), and inverse probability of censoring weighting (IPCW, Datta, Dong).
  • Evaluation of statistical power and estimation properties under varying censoring proportions.
  • Assessment of the 'net benefit' measure as a treatment effect estimator.

Main Results:

  • Methods ignoring uninformative pairs demonstrate comparable power to complex methods in low censoring scenarios.
  • These simpler methods show superior performance with high censoring proportions (>40%).
  • Harrell's c-index is an unbiased estimator for net benefit under proportional hazards; imputation or IPCW methods are unbiased up to 60% censoring.

Conclusions:

  • Simpler GPC methods (ignoring uninformative pairs) are efficient and powerful for censored survival data, particularly with high censoring rates.
  • The choice of GPC method depends on the specific research question (hypothesis testing vs. estimation) and the extent of censoring.
  • Imputation or IPCW methods are recommended for unbiased net benefit estimation when proportional hazards do not hold and censoring is substantial.