Statistical analysis of two arm randomized pre-post designs with one post-treatment measurement

• Published in: BMC medical research methodology Vol. 21; no. 1; pp. 1 - 16
• Main Author: Wan, Fei
• Format: Journal Article
• Language: English
• Published: London BioMed Central 24.07.2021; BMC
• Subjects: Analysis of covariance; ANCOVA; ANOVA; Change score; Design; Medical research; Pre-post design; Repeated measures; Research methodology; Statistical methods; Treatment effect; Variance analysis; Within-subjects design; Women
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-021-01323-9

Background Randomized pre-post designs, with outcomes measured at baseline and after treatment, have been commonly used to compare the clinical effectiveness of two competing treatments. There are vast, but often conflicting, amount of information in current literature about the best analytic methods for pre-post designs. It is challenging for applied researchers to make an informed choice. Methods We discuss six methods commonly used in literature: one way analysis of variance (“ANOVA”), analysis of covariance main effect and interaction models on the post-treatment score (“ANCOVAI” and “ANCOVAII”), ANOVA on the change score between the baseline and post-treatment scores (“ANOVA-Change”), repeated measures (“RM”) and constrained repeated measures (“cRM”) models on the baseline and post-treatment scores as joint outcomes. We review a number of study endpoints in randomized pre-post designs and identify the mean difference in the post-treatment score as the common treatment effect that all six methods target. We delineate the underlying differences and connections between these competing methods in homogeneous and heterogeneous study populations. Results ANCOVA and cRM outperform other alternative methods because their treatment effect estimators have the smallest variances. cRM has comparable performance to ANCOVAI in the homogeneous scenario and to ANCOVAII in the heterogeneous scenario. In spite of that, ANCOVA has several advantages over cRM: i) the baseline score is adjusted as covariate because it is not an outcome by definition; ii) it is very convenient to incorporate other baseline variables and easy to handle complex heteroscedasticity patterns in a linear regression framework. Conclusions ANCOVA is a simple and the most efficient approach for analyzing pre-post randomized designs.