An IV for the RCT: using instrumental variables to adjust for treatment contamination in randomised controlled trials
BMJ 2010; 340 doi: https://doi.org/10.1136/bmj.c2073 (Published 04 May 2010) Cite this as: BMJ 2010;340:c2073
- Jeremy B Sussman, Robert Wood Johnson Foundation clinical scholar12,
- Rodney A Hayward, professor of medicine and public health12
- 1Robert Wood Johnson Clinical Scholars Program, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
- 2Department of Veterans Affairs, Veterans Affairs Health Services Research and Development Center of Excellence, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan, USA
- Correspondence to: J B Sussman jeremysu{at}med.umich.edu
- Accepted 14 November 2009
Abstract
Although the randomised controlled trial is the “gold standard” for studying the efficacy and safety of medical treatments, it is not necessarily free from bias. When patients do not follow the protocol for their assigned treatment, the resultant “treatment contamination” can produce misleading findings. The methods used historically to deal with this problem, the “as treated” and “per protocol” analysis techniques, are flawed and inaccurate. Intention to treat analysis is the solution most often used to analyse randomised controlled trials, but this approach ignores this issue of treatment contamination. Intention to treat analysis estimates the effect of recommending a treatment to study participants, not the effect of the treatment on those study participants who actually received it. In this article, we describe a simple yet rarely used analytical technique, the “contamination adjusted intention to treat analysis,” which complements the intention to treat approach by producing a better estimate of the benefits and harms of receiving a treatment. This method uses the statistical technique of instrumental variable analysis to address contamination. We discuss the strengths and limitations of the current methods of addressing treatment contamination and the contamination adjusted intention to treat technique, provide examples of effective uses, and discuss how using estimates generated by contamination adjusted intention to treat analysis can improve clinical decision making and patient care.
Footnotes
The authors thank Michelle Heisler, David Kent, and Joshua Angrist for comments on earlier versions of this work.
Contributors: RAH conceived of the idea for this article and was involved in every stage of writing and editing. JBS was involved in all stages of research review, writing, and editing. Both authors act as guarantors.
Funding: This work was supported in part by the Robert Wood Johnson Clinical Scholars Program, the Department of Veteran Affairs Cooperative Studies Program (CSP #465 FS), and the Measurement Core of the Michigan Diabetes Research & Training Center (NIDDK of The National Institutes of Health [P60 DK-20572]). None of the funders had any role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.
Competing interests: Both authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: (1) No financial support for the submitted work from anyone other than their employer; (2) No financial relationships with commercial entities that might have an interest in the submitted work; (3) No spouses, partners, or children with relationships with commercial entities that might have an interest in the submitted work; (4) No non-financial interests that may be relevant to the submitted work.
Provenance and peer review: Not commissioned; externally peer reviewed.