๐ค AI Summary
็ ็ฉถๆข่ฎจไบๅจๅๅ้้ๅบ้ๆบๅไธญ็ฆปๆฃๅ่ฟ็ปญๅๅ้็ๅฝฑๅ๏ผ้่ฟ็่ฎบๅๆๅๅฎ่ฏ็ ็ฉถๆๅบ็ฆปๆฃๅๅขๅผบ็จณๅฅๆงไฝๅฏ่ฝ้ไฝๆ็ใ
๐ Abstract
Covariate-adaptive randomization(CAR) is widely implemented in clinical trials to balance prognostic covariates across treatment arms. Continuous covariates are often discretized into strata in practice, yet their consequences are not clearly understood. This paper provides a comprehensive study of the impact of discretization on both the CAR design process and the inferential results thereafter. We establish the asymptotic properties of both imbalance measures and treatment effect estimators under discretized and non-discretized settings. Practical recommendations are given on when and how discretization should be employed. We show that discretization in design is generally recommended, as it enhances robustness against model misspecification. However, if the true model is known, the most efficient strategy is to balance covariates according to that model in the design. The theoretical results are corroborated by extensive simulation studies and an empirical application to a diabetes trial dataset. Together, the results clarify the gains and losses of discretization in CAR and pave the way for learning impact of discretization to other designs and beyond.