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. 2018 Mar 28:10:80-85.
doi: 10.1016/j.conctc.2018.03.008. eCollection 2018 Jun.

Different ways to estimate treatment effects in randomised controlled trials

Twisk J  1 , Bosman L  1 , Hoekstra T  1   2 , Rijnhart J  1 , Welten M  1 , Heymans M  1
Affiliations

Different ways to estimate treatment effects in randomised controlled trials

Twisk J et al. Contemp Clin Trials Commun. .

Erratum in

Abstract

Background: Regarding the analysis of RCT data there is a debate going on whether an adjustment for the baseline value of the outcome variable should be made. When an adjustment is made, there is a lot of misunderstanding regarding the way this should be done. Therefore, the aims of this educational paper are: 1) to explain different methods used to estimate treatment effects in RCTs, 2) to illustrate the different methods with a real life example and 3) to give an advise on how to analyse RCT data.

Methods: Longitudinal analysis of covariance, repeated measures analysis in which also the baseline value is used as outcome and the analysis of changes were theoretically explained and applied to an example dataset investigating a systolic blood pressure lowering treatment.

Results: It was shown that differences at baseline should be taken into account and that regular repeated measures analysis and regular analysis of changes did not adjust for the baseline differences between the groups and therefore lead to biased estimates of the treatment effect. In the real life example, due to the differences at baseline between the treatment and control group, the different methods lead to different estimates of the treatment effect.

Conclusion: Regarding the analysis of RCT data, it is advised to use longitudinal analysis of covariance or a repeated measures analysis without the treatment variable, but with the interaction between treatment and time in the model.

Keywords: Analysis of changes; Longitudinal of covariance; Randomised controlled trials; Regression to the mean; Repeated measures.

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Figures

Fig. 1
Fig. 1
Mathematical equivalence between longitudinal analysis of covariance and the analysis of changes with an adjustment for baseline differences.
Fig. 2
Fig. 2
Illustration of the problem of non-collapsibility of the OR. a) no differences between intervention and control (OR (intervention/control) = 1).
See this image and copyright information in PMC

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