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Abstract:
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Following the generalized pairwise comparisons, the win statistics (i.e., win ratio, win odds and net benefit) are advantageous in analyzing and interpreting a composite of multiple outcomes in clinical trials. The win ratio (ratio of win proportions), the net benefit (difference in win proportions) and the win odds (odds of win proportions) are derived using the same win proportions; and they test the same hypothesis of equal win probabilities between the two groups. For time-to-event outcomes, however, censoring may introduce bias. In this talk, we will first present impact of follow-up time. If the treatment has a long-term benefit from a more important but less frequent endpoint (eg, death), the win statistics can show such benefit by following patients longer. We compare the three win statistics to decide whether they complement one another in demonstrating treatment effect, with respect to amount of censoring and the IPCW (inverse-probability-of-censoring weighting) adjustment.
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