Time-constant absolute effect measures for time-to-event outcomes
Abstract
Reporting treatment effects from clinical trials on both relative and absolute scales is crucial. While absolute measures like the Number Needed to Treat (NNT) are well-established for binary outcomes, their calculation for time-to-event outcomes remains challenging due to time-dependence, which hinders interpretation and communication. Traditional additive hazard models, while addressing time-dependence, have been limited by restrictive assumptions regarding outcome distributions. This paper proposes to use a recently introduced class of parametric additive hazard models to compute time-constant absolute effect measures for time-to-event outcomes. These models allow for a wide range of parametric distributions, overcoming the limitations of previous approaches. The approach provides a single, absolute effect size (e.g., hazard difference or NNT) summarizing the effect over the entire study duration. We illustrate this method using digitized Kaplan-Meier data from the EMPA-REG OUTCOME trial, focusing on all-cause mortality, and fit six different parametric distributions (exponential, linear hazard rate, Weibull, log-logistic, Gompertz, and Gamma-Gompertz). Despite notable differences in model fit across the six distributions, the estimated rate differences, corresponding NNTs, and their confidence intervals were remarkably similar. The linear hazard rate and Gompertz models, which provided the best fit according to the BIC, yielded a rate difference of -8.8 per 1,000…
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