The DOI matches its Crossref record; 32 of the 34 references it cites were found in a registry or an index.
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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Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
Identification of Causal Effects Using Instrumental Variables
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476902 · Cited by 4,230
Bowman, P. W. (1963) · AIBS Bulletin · doi:10.2307/1292957 · Cited by 3,654
Bayesian inference for causal effects: The role of randomization
Rubin, D. B. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344064 · Cited by 2,503
Principal Stratification in Causal Inference
Frangakis, C., & Rubin, D. B. (2002) · Biometrics · doi:10.1111/j.0006-341x.2002.00021.x · Cited by 1,543
Assignment to Treatment Group on the Basis of a Covariate
Rubin, D. B. (1977) · Journal of Educational Statistics · doi:10.3102/10769986002001001 · Cited by 1,362
An introduction to instrumental variables for epidemiologists
Greenland, S. (2000) · International Journal of Epidemiology · doi:10.1093/ije/29.4.722 · Cited by 1,156
Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models
Abadie, A. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502753479419 · Cited by 660
Bayesian inference for causal effects in randomized experiments with noncompliance
Imbens, G. W., & Rubin, D. B. (1997) · The Annals of Statistics · doi:10.1214/aos/1034276631 · Cited by 538
Research works printed but not linked: 2
- Baker S. G., 1997, The Encyclopedia of Statistical Science, 134
- Robins J. M., 1989, Health Service Research Methodology: A Focus on AIDS, 113
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Regression and CausalityPreprint
Schomaker, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.11754 · Cited by 4
Little, R. J. A., & Rubin, D. B. (2019) · Wiley series in probability and statistics · doi:10.1002/9781119482260.refs
Self-citationGrellety, E., Shepherd, S., Roederer, T., Manzo, M. L., Doyon, S., Ategbo, E. A., & Grais, R. F. (2013) · PLoS ONE · doi:10.1371/annotation/d41cce68-f8a3-45f1-beed-c2daaa938b88
Metcalfe, J. D., & Drake, R. E. (2025) · Psychiatric Services · doi:10.1176/appi.ps.20250067
Yang, Z., & Nanda, S. (2025) · Contemporary Clinical Trials · doi:10.1016/j.cct.2025.107982
Edwards, T., Thompson, J. A., Opondo, C., & Allen, E. (2025) · Clinical Trials · doi:10.1177/17407745251378407
“3,10 In brief, these possible underlying principal compliance status categories are (as illustrated in Table 1) ‘complier’ (would always comply with the allocation they are assigned to), ‘always-taker’ (would always take the intervention offered or seek to receive it, regardless of trial…”
InfluentialGould, R. L., Callaghan, T., White, D., Wetherell, J. L., Bursnall, M., Bradburn, M., Wailoo, A., Serfaty, M. A., Graham, C. D., Howard, R. J., et al. (2026) · BMC Geriatrics · doi:10.1186/s12877-026-07094-6
Nguyễn, P. T., Tran, L. M., Khuong, L. Q., Nguyen, P. T., Nguyen, B. V., Be, T. H., Young, M. F., DiGirolamo, A. M., Martorell, R., & Ramakrishnan, U. (2026) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2026.101270
Large trials of psychosocial interventions: examples from individual placement and support
Metcalfe, J. D., Mueser, K. T., & Drake, R. E. (2026) · International Journal of Public Health · doi:10.3389/ijph.2026.1609143
“These include per-protocol (PP) and as-treated (AT) analyses [61] and causal methods such as instrumental variables and complier adjusted causal effect estimation [62, 63].”
Causal estimation of interval-censored failure time data with a binary instrumental variable
Lou, Y., Du, M., Wang, P., & Song, X. (2026) · Test · doi:10.1007/s11749-026-01031-w
Causal modelling of survival data with informative noncompliance
Odondi, L. (2011) · Research Explorer (The University of Manchester)
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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