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

As an extension of the Mann-Whitney approach in the randomized controlled trial (RCT) setting, generalized pairwise comparisons (GPC) methods are based on assigning scores to pairs of subjects where all pairs of treatment and control subjects are evaluated: the outcome of every individual in the treatment group is compared with the outcome of every individual in the control group. The GPC test statistic can, therefore, be expressed as a treatment effect by such measures as the net benefit, win odds, win ratio (WR), or probability index for the therapeutic intervention. Taking the WR as an example for this study, it has an attractive interpretation as the inverse of the hazard ratio under proportional hazards. However, its estimate could be biased in the presence of substantial censoring and cautious interpretation is needed. Considerable censoring increases the numbers of indeterminate treatment and control pairs, where the win or loss is undetermined due to the censored observation(s) and a definitive score cannot be assigned. Such indeterminant pairs are typically treated as "ties" and scored as 0. We propose a novel approach leveraging pseudo-observation values to address this issue of ties resulting from censoring for a single time-to-event outcome. We demonstrate and compare the performance measures of our method with existing GPC methods in Monte Carlo simulations under various equal drop-out, unequal drop-out, and administrative censoring scenarios. Moreover, we illustrate this new approach using two reconstructed datasets from an oncology and cardiomyopathy RCT.

Mid-Level

Knowledge

Women in Statistics and Data Science 2024