Abstract
The intention-to-treat analysis is the gold standard for evaluating the efficacy in a randomized controlled trial. However, when non-adherence to randomized treatments is high the actual treatment effect may be underestimated. The impact of drop-out from the intervention group or drop-in to the control group may be controlled by trial design, increasing the sample size, effective study execution, and a pre-specified analytical plan to take contamination into account.
These analyses may include censoring at the time of co-interventions associated with stopping treatment, lag censoring which allows an additional period after discontinuation of study treatment to account for residual treatment effects, inverse probability of censoring weights (IPCW), accelerated failure time models, and contamination adjusted intent-to-treat analysis . These methods are particularly useful in assessing the “prescribed efficacy” of the study treatment, which can aid clinical decision-making .
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References
Sussman JB, Hayward RA (2010) Using instrumental variables to adjust for treatment contamination in randomized controlled trials. BMJ 340:c2073. https://doi.org/10.1136/bmj.c2073
Schulz KF, Altman DG, Moher D (2010) CONSORT 2010 Statement: updated guidelines for reporting parallel group randomized trials. BMJ 340:c332
Gupta SK (2011) Intention-to-treat concept: a review. Perspect Res 2:109–112
Robins JM, Finkelstein DM (2000) Correcting for noncompliance and dependent censoring in an AIDS Clinical Trial with inverse probability of censoring weighted (IPCW) long-rank tests. Biometrics 56:779–788
Hernan MA, Brumback B, Robins JM (2000) Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 11:561–570
Robins JM, Hernan MA, Brumbach B (2000) Marginal structural models and causal inference in epidemiology. Epidemiology 11:550–560
Morden JP, Lambert PC, Latimer N, Abrams KR, Wailoo AJ (2011) Assessing methods for dealing with treatment switching in randomized controlled trials: a simulation study. BMC Med Res Methodol 11:4
Greenland S, Lanes S, Jara M (2008) Estimating effects from randomized trials with discontinuations: the need for intent-to-treat design and G-estimation. Clin Trials 5:5–13
Branson M, Whitehead J (2002) Estimating a treatment effect in survival studies in which patients switch treatment. Stat Med 21:2449–2463
Kubo Y, Sterling LR, Parfrey PS, Gill K, Mahaffey KW, Gioni I, Trotman M-L, Dehmel B, Chertow GM (2015) Assessing the treatment effect in a randomized controlled trial with extensive non-adherence: the EVOLVE trial. Pharm Stat 14:242–251
Chertow GM, Block GA, Correa-Rotter R et al (2012) Effect of cinacalcet on cardiovascular disease in patients undergoing dialysis. New Engl J Med 367:2482–2494
Chertow GM, Pupim LB, Block GA, Correa-Rotter R, Drueke TB, Floege J, Goodman WG, London GM, Mahaffey KW, Moe SM, Wheeler DC, Albizem M, Olson K, Klassen P, Parfrey P (2007) Evaluation of Cinacalcet Therapy to Lower Cardiovascular Events (EVOLVE): rationale and design overview. CJASN 2:898–905
Snapinn SM, Jiang Q, Iglewicz B (2004) Informative noncompliance in endpoint trials. Curr Control Trials Cardiovasc Med 5:5
Parfrey PS, Block GA, Correa-Rotter R, Drueke TB, Floege J, Herzog CA, London GM, Mahaffey KW, Moe SM, Wheeler DC, Chertow GM (2016) Lessons learned from EVOLVE for planning of future randomized trials in patients on dialysis. Clin J Am Soc Nephrol 11:539–546
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Parfrey, P.S. (2021). Randomized Controlled Trials 7: On Contamination and Estimating the Actual Treatment Effect. In: Parfrey, P.S., Barrett, B.J. (eds) Clinical Epidemiology. Methods in Molecular Biology, vol 2249. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1138-8_17
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DOI: https://doi.org/10.1007/978-1-0716-1138-8_17
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