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<p>Pre-post designs are widely used in clinical trials and experimental studies to assess the effectiveness of treatments. Common statistical methods for analyzing pre-post data include analysis of variance (ANOVA) using post-treatment or the change from baseline, analysis of covariance (ANCOVA) with homogeneous or heterogeneous slopes, and linear mixed models (LMM). While numerous studies have compared these methods, limited studies have investigated the impact of adjusting for influential baseline covariates under different randomization approaches. In this study, we conducted a series of comprehensive simulation studies to investigate the impact of adjusting baseline covariates under several randomization approaches: simple randomization, stratified block randomization, and covariate adaptive randomization using the minimization method by Pocock and Simon. Results demonstrated that when no covariates were considered in the randomization approach, the two ANCOVA methods always have good performance. Adjusting for relevant baseline covariates led to substantial power gains, with the extent of these gains depending on the size of the covariate effects and the randomization approach employed. Stratified block randomization and covariate adaptive randomization consistently outperformed simple randomization in terms of power gains after adjusting for covariates, with covariate adaptive randomization becoming more superior as the number of covariates increased.</p>

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The impact of randomization techniques on the performance of pre-post design models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月19日

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  • Xinlin Lu,
  • Yahui Zhang,
  • Samuel S. Wu,
  • Guogen Shan

摘要

Pre-post designs are widely used in clinical trials and experimental studies to assess the effectiveness of treatments. Common statistical methods for analyzing pre-post data include analysis of variance (ANOVA) using post-treatment or the change from baseline, analysis of covariance (ANCOVA) with homogeneous or heterogeneous slopes, and linear mixed models (LMM). While numerous studies have compared these methods, limited studies have investigated the impact of adjusting for influential baseline covariates under different randomization approaches. In this study, we conducted a series of comprehensive simulation studies to investigate the impact of adjusting baseline covariates under several randomization approaches: simple randomization, stratified block randomization, and covariate adaptive randomization using the minimization method by Pocock and Simon. Results demonstrated that when no covariates were considered in the randomization approach, the two ANCOVA methods always have good performance. Adjusting for relevant baseline covariates led to substantial power gains, with the extent of these gains depending on the size of the covariate effects and the randomization approach employed. Stratified block randomization and covariate adaptive randomization consistently outperformed simple randomization in terms of power gains after adjusting for covariates, with covariate adaptive randomization becoming more superior as the number of covariates increased.