Application of causal inference methods in the analyses of randomised controlled trials: a systematic review

Ruth E. Farmer*, Daphne Kounali, A. Sarah Walker, Jelena Savović, Alison Richards, Margaret T. May, Deborah Ford

*Corresponding author for this work

Research output: Contribution to journalReview article (Academic Journal)peer-review

12 Citations (Scopus)
302 Downloads (Pure)


Background: Applications of causal inference methods to randomised controlled trial (RCT) data have usually focused on adjusting for compliance with the randomised intervention rather than on using RCT data to address other, non-randomised questions. In this paper we review use of causal inference methods to assess the impact of aspects of patient management other than the randomised intervention in RCTs. Methods: We identified papers that used causal inference methodology in RCT data from Medline, Premedline, Embase, Cochrane Library, and Web of Science from 1986 to September 2014, using a forward citation search of five seminal papers, and a keyword search. We did not include studies where inverse probability weighting was used solely to balance baseline characteristics, adjust for loss to follow-up or adjust for non-compliance to randomised treatment. Studies where the exposure could not be assigned were also excluded. Results: There were 25 papers identified. Nearly half the papers (11/25) estimated the causal effect of concomitant medication on outcome. The remainder were concerned with post-randomisation treatment regimens (sequential treatments, n =5), effects of treatment timing (n = 2) and treatment dosing or duration (n = 7). Examples were found in cardiovascular disease (n = 5), HIV (n = 7), cancer (n = 6), mental health (n = 4), paediatrics (n = 2) and transfusion medicine (n = 1). The most common method implemented was a marginal structural model with inverse probability of treatment weighting. Conclusions: Examples of studies which exploit RCT data to address non-randomised questions using causal inference methodology remain relatively limited, despite the growth in methodological development and increasing utilisation in observational studies. Further efforts may be needed to promote use of causal methods to address additional clinical questions within RCTs to maximise their value.

Original languageEnglish
Article number23
Number of pages14
Issue number1
Publication statusPublished - 10 Jan 2018


  • Causal inference
  • G-computation
  • G-estimation
  • Marginal nested models
  • Marginal structural models
  • RCT
  • Systematic review
  • Time-dependent confounding


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