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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Covariate adjustment in randomized controlled trials: General concepts and practical considerations.

Kelly Van Lancker¹, Frank Bretz^2,3, Oliver Dukes¹

¹Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.

Clinical Trials (London, England)

|June 3, 2024

Summary

Covariate adjustment in randomized controlled trials enhances precision by distinguishing between conditional and marginal treatment effects. Standardization is recommended for robust and efficient estimation of marginal effects.

Keywords:

Baseline covariates covariate adjustment efficiency gain estimands

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Area of Science:

Biostatistics
Clinical Trials Methodology
Epidemiology

Background:

Growing interest in covariate adjustment for randomized controlled trials (RCTs).
US Food and Drug Administration guidance highlights conditional vs. marginal treatment effects.
Distinction between effects often overlooked in clinical practice.

Purpose of the Study:

Review covariate adjustment in RCTs for enhanced precision.
Clarify differences between conditional and marginal estimands.
Align statistical methods with chosen estimands.

Main Methods:

Describe conditional and marginal estimands.
Highlight misalignment in common methods for marginal effects.
Advocate for standardization approach.

Main Results:

Covariate adjustment can improve precision in RCTs.
Standardization leverages baseline covariates for efficiency.
Standardization is robust to model misspecification.

Conclusions:

Aligning analysis with estimands is crucial.
Standardization offers an efficient and robust method for marginal effect estimation.
Practical considerations for covariate adjustment in RCTs.