Intention-to-treat approach to data from randomized controlled trials: a sensitivity analysis

Wright, C.C.; Sim, J.

doi:10.1016/s0895-4356(03)00155-0

Cited by 171Statistical Methods in Clinical TrialsAdvanced Causal Inference TechniquesStatistical Methods and Bayesian Inference

Intention-to-treat approach to data from randomized controlled trials: a sensitivity analysis

Wright, C.C., Sim, J.·2003·Journal of Clinical Epidemiology

DOI 10.1016/s0895-4356(03)00155-0

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Abstract

The intention-to-treat (ITT) approach to randomized controlled trials analyzes data on the basis of treatment assignment, not treatment receipt. Alternative approaches make comparisons according to the treatment received at the end of the trial (as-treated analysis) or using only subjects who did not deviate from the assigned treatment (adherers-only analysis). Using a sensitivity analysis on data for a hypothetical trial, we compare these different analytical approaches in the context of two common protocol deviations: loss to follow-up and switching across treatments. In each case, two rates of deviation are considered: 10% and 30%. The analysis shows that biased estimates of effect may occur when deviation is nonrandom, when a large percentage of participants switch treatments or are lost to follow-up, and when the method of estimating missing values accounts inadequately for the process causing loss to follow-up. In general, ITT analysis attenuates between-group effects. Trialists should use sensitivity analyses on their data and should compare the characteristics of participants who do and those who do not deviate from the trial protocol. The ITT approach is not a remedy for unsound design, and imputation of missing values is not a substitute for complete, good quality data.

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“It is true that these analyses suffer from selection bias, but they will allow us to reach conclusions about the efficacy of the treatment in patients who complete all the intervention modules [92].”
From Protocol for a randomized controlled dismantling study of an internet-based intervention for depressive symptoms: Exploring the contribution of behavioral activation and positive psychotherapy strategies (2019) · cites this paper as [92]
Per protocol analyses allow conclusions about the efficacy of the treatment in patients who complete all the intervention modules.
“It is true that these analyses suffer from selection bias, but they will allow us to reach conclusions about the efficacy of the treatment in patients who complete all the intervention modules [92].”
From Protocol for a randomized controlled dismantling study of an internet-based intervention for depressive symptoms: Exploring the contribution of behavioral activation and positive psychotherapy strategies (2019) · cites this paper as [92]

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Authors: Wright, C. C., & Sim, J.
Affiliations: Coventry University (GB)
Keele University (GB)
Corresponding author: Wright, C.C.
ORCID: Sim, J.
Venue: Journal of Clinical Epidemiology
Publisher: Elsevier BV
Published: 2003-09
Volume: 56
Issue: 9
Pages: 833-842
ISSN: 0895-4356 (ISSN-L)
DOI: 10.1016/s0895-4356(03)00155-0
OpenAlex: W2070581218
Cited by: 171
References: 48
Citations by year: 2026: 2 · 2025: 1 · 2024: 7 · 2023: 5 · 2022: 4 · 2021: 10 · 2020: 6 · 2019: 6 · 2018: 2 · 2017: 7 · 2016: 12 · 2015: 11 · 2014: 15 · 2013: 12 · 2012: 8
FWCI: 7.51
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of Clinical Epidemiology (publishedVersion)
PubMed (publishedVersion)
University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords: intention-to-treat analysis, protocol deviations, loss to follow-up, treatment switching, as-treated analysis, missing data imputation, bias in treatment effect estimation, sensitivity analysis, randomized controlled trials
MeSH terms: Data Interpretation, Statistical, Follow-Up Studies, Humans, Patient Acceptance of Health Care, Sensitivity and Specificity, Randomized Controlled Trials as Topic, Patient Selection, Evidence-Based Medicine

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Intention-to-treat approach to data from randomized controlled trials: a sensitivity analysis

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