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Application of causal inference methods in the analyses of randomised controlled trials: a systematic review.
Farmer, Ruth E; Kounali, Daphne; Walker, A Sarah; Savovic, Jelena; Richards, Alison; May, Margaret T; Ford, Deborah.
Affiliation
  • Farmer RE; MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL School of Life and Medical Sciences, London, UK. ruth.farmer@lshtm.ac.uk.
  • Kounali D; Department of Non-communicable Diseases Epidemiology, London School of Hygiene & Tropical Medicine, London, UK. ruth.farmer@lshtm.ac.uk.
  • Walker AS; Bristol Medical School, University of Bristol, Bristol, UK.
  • Savovic J; MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL School of Life and Medical Sciences, London, UK.
  • Richards A; Bristol Medical School, University of Bristol, Bristol, UK.
  • May MT; The National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care West (NIHR CLAHRC West) at University Hospitals Bristol NHS Foundation Trust, Bristol, UK.
  • Ford D; Bristol Medical School, University of Bristol, Bristol, UK.
Trials ; 19(1): 23, 2018 Jan 10.
Article in En | MEDLINE | ID: mdl-29321046
ABSTRACT

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.
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Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Randomized Controlled Trials as Topic / Data Analysis Type of study: Clinical_trials / Observational_studies / Prognostic_studies / Systematic_reviews Limits: Humans Language: En Journal: Trials Journal subject: MEDICINA / TERAPEUTICA Year: 2018 Document type: Article Affiliation country: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Randomized Controlled Trials as Topic / Data Analysis Type of study: Clinical_trials / Observational_studies / Prognostic_studies / Systematic_reviews Limits: Humans Language: En Journal: Trials Journal subject: MEDICINA / TERAPEUTICA Year: 2018 Document type: Article Affiliation country: United kingdom
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