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.
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BibTeX
@misc{rand2026rerandomizationprobability,
title = {{Re-randomization-probability weighting for population-adjusted comparisons of maintenance outcomes across trial designs}},
author = {Kim Rand},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.12074},
eprint = {2610.12074},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.12074},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Re-randomization-probability weighting for population-adjusted comparisons of maintenance outcomes across trial designs
AU - Kim Rand
PY - 2026
DA - 2026/10/08
PB - arXiv
DO - 10.48550/arxiv.2610.12074
UR - https://arxiv.org/abs/2610.12074
AB - 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.
ER -