UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Strengthening causal inference from randomised controlled trials of complex interventions

Leroy, Jef L; Frongillo, Edward A; Kase, Bezawit E; Alonso, Silvia; Chen, Mario; Dohoo, Ian; Huybregts, Lieven; ... Saville, Naomi M; + view all (2022) Strengthening causal inference from randomised controlled trials of complex interventions. BMG Global Health , 7 (6) , Article e008597. 10.1136/bmjgh-2022-008597. Green open access

[thumbnail of Saville_Strengthening causal inference from randomised controlled trials of complex interventions_VoR.pdf]
Preview
Text
Saville_Strengthening causal inference from randomised controlled trials of complex interventions_VoR.pdf - Published Version

Download (435kB) | Preview

Abstract

Researchers conducting randomised controlled trials (RCTs) of complex interventions face design and analytical challenges that are not fully addressed in existing guidelines. Further guidance is needed to help ensure that these trials of complex interventions are conducted to the highest scientific standards while maximising the evidence that can be extracted from each trial. The key challenge is how to manage the multiplicity of outcomes required for the trial while minimising false positive and false negative findings. To address this challenge, we formulate three principles to conduct RCTs: (1) outcomes chosen should be driven by the intent and programme theory of the intervention and should thus be linked to testable hypotheses; (2) outcomes should be adequately powered and (3) researchers must be explicit and fully transparent about all outcomes and hypotheses before the trial is started and when the results are reported. Multiplicity in trials of complex interventions should be managed through careful planning and interpretation rather than through post hoc analytical adjustment. For trials of complex interventions, the distinction between primary and secondary outcomes as defined in current guidelines does not adequately protect against false positive and negative findings. Primary outcomes should be defined as outcomes that are relevant based on the intervention intent and programme theory, declared (ie, registered), and adequately powered. The possibility of confirmatory causal inference is limited to these outcomes. All other outcomes (either undeclared and/or inadequately powered) are secondary and inference relative to these outcomes will be exploratory.

Type: Article
Title: Strengthening causal inference from randomised controlled trials of complex interventions
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1136/bmjgh-2022-008597
Publisher version: http://dx.doi.org/10.1136/bmjgh-2022-008597
Language: English
Additional information: https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
Keywords: Science & Technology, Life Sciences & Biomedicine, Public, Environmental & Occupational Health, randomised control trial, intervention study, PROGRAM, IMPLEMENTATION, MULTIPLICITY, GUIDELINES, OUTCOMES, WEIGHT, INFANT, IMPACT
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10151859
Downloads since deposit
15Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item