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A Comparison of Methods for Estimating the Causal Effect of a Treatment in Randomized Clinical Trials Subject to Noncompliance
Open accessAbstract
Summary We consider the analysis of clinical trials that involve randomization to an active treatment (T = 1) or a control treatment (T = 0), when the active treatment is subject to all‐or‐nothing compliance. We compare three approaches to estimating treatment efficacy in this situation: as‐treated analysis, per‐protocol analysis, and instrumental variable (IV) estimation, where the treatment effect is estimated using the randomization indicator as an IV. Both model‐ and method‐of‐moment based IV estimators are considered. The assumptions underlying these estimators are assessed, standard errors and mean squared errors of the estimates are compared, and design implications of the three methods are examined. Extensions of the methods to include observed covariates are then discussed, emphasizing the role of compliance propensity methods and the contrasting role of covariates in these extensions. Methods are illustrated on data from the Women Take Pride study, an assessment of behavioral treatments for women with heart disease.
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“The impact of non-adherence (i.e. non-uptake of ACT in the intervention group) and contamination (i.e. delivery of psychological therapy in the control group) will be assessed using complier-average causal effect analysis and a per-protocol analysis. Average Causal Response analysis will be used to assess any incremental impacts of the number of ACT sessions received [65].”
From A randomised controlled trial of acceptance and commitment therapy plus usual care compared to usual care alone for improving psychological health in people with motor neuron disease (COMMEND): study protocol. (2022) · cites this paper as [65]
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“Finally, we ran regression models with and without taking advantage of the randomized assignment to treatment as an instrument [59, 60], and also as a covariate to control for non-compliance in the models using the SP Impact Evaluation Survey.”
MethodFerrara, A., Hedderson, M. M., Brown, S. D., Albright, C. L., Ehrlich, S. F., Tsai, A.-L., Caan, B. J., Sternfeld, B., Gordon, N. P., Schmittdiel, J. A., et al. (2016) · Diabetes Care · doi:10.2337/dc15-1254 · Cited by 171
“across number of sessions completed and using an instrumental variable to estimate the complier average causal effect, a measure of the effectiveness of the intervention among those who comply with assigned treatment, with the randomization indicator used as the instrumental variable (24).”
MethodDo board interlocks increase innovation? Evidence from a corporate governance reform in India
Christian Helmers, B., Patnam, M., & Rau, P. R. (2017) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2017.04.001 · Cited by 169
Lintvedt, O. K., Griffiths, K. M., Sørensen, K., Østvik, A. R., Wang, C. E. A., Eisemann, M., & Waterloo, K. (2013) · Clinical Psychology & Psychotherapy · doi:10.1002/cpp.770 · Cited by 157
Metadata
- Authors
- Little, R. J., Long, Q., & Lin, X.
- Affiliations
- University of Michigan (US)
- Emory University (US)
- Harvard University (US)
- Corresponding author
- Little, Roderick J.
- Long, Qi
- Lin, Xihong
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-05-28
- Volume
- 65
- Issue
- 2
- Pages
- 640-649
- ISSN
- 0006-341X (ISSN-L)
- 1541-0420
- OpenAlex
- W2144385713
- Cited by
- 111
- References
- 34
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 3 · 2023: 4 · 2022: 6 · 2021: 4 · 2020: 8 · 2019: 11 · 2018: 6 · 2017: 3 · 2016: 5 · 2015: 9 · 2014: 6 · 2013: 8 · 2012: 8
- FWCI
- 5.95
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- Biometrics (publishedVersion)
- PubMed (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Collection of Biostatistics Research Archive (submittedVersion)
- Funders
- National Cancer Institute (R01CA076404)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- treatment non-adherence, causal effect estimation, randomized controlled trials, as-treated analysis, per-protocol analysis, instrumental variable estimation
- MeSH terms
- Algorithms, Biometry, Computer Simulation, Data Interpretation, Statistical, Patient Compliance, Models, Statistical, Epidemiologic Research Design, Causality, Proportional Hazards Models, Randomized Controlled Trials as Topic, Outcome Assessment, Health Care
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