Re-randomization-probability weighting for population-adjusted comparisons of maintenance outcomes across trial designs

Abstract: Randomized-withdrawal trials re-randomize induction responders to continued treatment or withdrawal. Baseline characteristics are often reported for the induction population but unavailable for responders, limiting population-adjusted indirect comparisons of maintenance outcomes. We describe re-randomization-probability weighting, which combines induction-population calibration with inverse weighting by known maintenance assignment probabilities. Coding induction non-response as failure defines durable response: response at both induction and maintenance. The design weights reproduce induction-population covariate totals in randomization expectation, allowing population adjustment using reported induction baselines. Conditional maintenance probabilities can also be estimated, although different induction treatments generate different responder populations. A synthetic three-trial demonstration reduced durable-response bias from 0.106 to 0.001 and from 0.055 to 0.001 in two comparisons. Full-procedure simulations showed interval coverage of 91.5% - 94.3%; stress scenarios illustrated bias from omitted covariates and incorrect assignment weights. The method connects established design weighting and aggregate dilution approaches to population-adjusted comparison across randomized-withdrawal and treat-through designs. It requires compatible durable-response endpoints and credible transport and modeling assumptions. Withdrawal after different induction regimens is not automatically a common comparator. Where no valid anchor exists, unanchored comparison is appropriate only if its demanding adjustment assumptions are defensible.