Population-Adjusted Survival: A Real-World, Epidemiologic Approach for Analyzing Time-to-Event Clinical Trial Data

Results of a clinical trial may misrepresent the target population. Inappropriate inclusion/exclusion criteria, selective recruitment, and differential participation may restrict the number of patients who consent to join the study. Frequently, they differ from the real-world population in terms of key outcome-related characteristics. Such biased selection may make the therapeutic agent seem more or less effective within a certain stratum. While block randomization and an intent-to-treat design can help to minimize confounding within the trial sample, differences may still exist with respect to the distribution of these variables in the population. To mitigate misleading conclusions, the current manuscript illustrates how to ‘population-adjust’ survival estimates in a clinical trial using the Beckett–Bayes expansion method. In contrast to other population adjustment methods, this novel approach involves substituting the probability of belonging to a specific sample stratum with the corresponding population data (i.e., population borrowing). As a regression-free technique, the method is not susceptible to model misspecification. A large clinical trial focusing on cardiovascular-related deaths in patients with hypertension is used as an example to illustrate the method. The proposed framework is promising and easy to implement when reliable population-level information is available.