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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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The optimal pre-post allocation for randomized clinical trials.

Shiyang Ma1,2, Tianying Wang3,4

  • 1Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

BMC Medical Research Methodology
|March 28, 2023
PubMed
Summary
This summary is machine-generated.

Repeating baseline and follow-up measurements in pre-post studies improves treatment effect detection. Optimal allocation of these repeated measures minimizes sample size and maximizes statistical power in clinical trials.

Keywords:
Analysis of covarianceOptimal allocationPre-post designRepeated measuresRepeating baselines

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • Analysis of covariance (ANCOVA) is standard for pre-post designs with continuous variables.
  • Repeating measurements can enhance detection of treatment effects, especially with high variability.
  • While follow-up repeats are generally more advantageous, pre-treatment repeats also offer efficiency gains in clinical trials.

Purpose of the Study:

  • Investigate the utility of multiple pre-treatment and post-treatment measurements in randomized clinical trials.
  • Develop optimal experimental designs for allocating multiple pre-post measurements.
  • Derive formulas for sample size and power calculations under various correlation structures.

Main Methods:

  • Utilized sample size formulas for ANCOVA with pre-treatment means as covariates and follow-up means as responses.
  • Proposed an optimal experimental design for multiple pre-post allocations given a fixed number of visits.
  • Conducted Monte Carlo simulations for non-linear models where closed-form formulas are unavailable.

Main Results:

  • Theoretical formulas and simulations confirm benefits of repeating pre-treatment measurements in pre-post randomized studies.
  • The derived optimal pre-post allocation strategy is effective for ANCOVA.
  • The ANCOVA-derived optimal allocation extends to binary measurements using logistic regression and GEE in simulations.

Conclusions:

  • Repeating baseline and follow-up assessments is a valuable and efficient technique in pre-post study designs.
  • The proposed optimal pre-post allocation designs effectively minimize sample size.
  • Achieving maximum statistical power is possible through optimized allocation of repeated measurements.