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Estimating causal effects of internet interventions in the context of nonadherence
Örebro University, School of Law, Psychology and Social Work. Department of Behavioural Sciences and Learning, Linköping University, Linköping, Sweden.ORCID iD: 0000-0002-9736-8228
2020 (English)In: Internet Interventions, ISSN 2214-7829, Vol. 21, article id 100346Article in journal (Refereed) Published
Abstract [en]

A substantial proportion of participants who are offered internet-based psychological treatments in randomized trials do not adhere and may therefore not receive treatment. Despite the availability of justified statistical methods for causal inference in such situations, researchers often rely on analytical strategies that either ignore adherence altogether or fail to provide causal estimands. The objective of this paper is to provide a gentle nontechnical introduction to complier average causal effect (CACE) analysis, which, under clear assumptions, can provide a causal estimate of the effect of treatment for a subsample of compliers. The article begins with a brief review of the potential outcome model for causal inference. After clarifying assumptions and model specifications for CACE in the latent variable framework, data from a previously published trial of an internet-based psychological treatment for irritable bowel syndrome are used to demonstrate CACE-analysis. Several model extensions are then briefly reviewed. The paper offers practical recommendations on how to analyze randomized trials of internet interventions in the context of nonadherence. It is argued that CACE-analysis, whenever it is considered appropriate, should be carried out as a complement to the standard intention-to-treat analysis and that the format of internet-based treatments is particularly well suited to such an analytical approach.

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 21, article id 100346
Keywords [en]
Adherence, Complier average causal effect, Mixture modeling, Psychological treatment, Randomized trial, Structural equation modeling
National Category
Public Health, Global Health, Social Medicine and Epidemiology Psychology
Identifiers
URN: urn:nbn:se:oru:diva-86091DOI: 10.1016/j.invent.2020.100346ISI: 000573900800005PubMedID: 32983907Scopus ID: 2-s2.0-85090591935OAI: oai:DiVA.org:oru-86091DiVA, id: diva2:1471956
Available from: 2020-09-30 Created: 2020-09-30 Last updated: 2024-01-11Bibliographically approved

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Hesser, Hugo

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