title: Adjusting for non-compliance and contamination in randomized clinical trials.
abstract: A method of analysis is presented for estimating the magnitude of a treatment effect among compliers in a clinical trial which is asymptotically unbiased and respects the randomization. The approach is valid even when compliers have a different baseline risk than non-compliers. Adjustments for contamination (use of the treatment by individuals in the control arm) are also developed. When the baseline failure rates in non-compliers and contaminators are the same as those who accept their allocated treatment, the method produces larger treatment effects than an 'intent-to-treat' analysis, but the confidence limits are also wider, and (even without this assumption) asymptotically the efficiencies are the same. In addition to providing a better estimate of the true effect of a treatment in compliers, the method also provides a more realistic confidence interval, which can be especially important for trials aimed at showing the equivalence of two treatments. In this case the intent-to-treat analysis can give unrealistically narrow confidence intervals if substantial numbers of patients elect to have the treatment they were not randomized to receive.
authors.affiliation: Imperial Cancer Research Fund, London, UK.
authors.forename: J
authors.initials: J
authors.lastname: Cuzick
authors.forename: R
authors.initials: R
authors.lastname: Edwards
authors.forename: N
authors.initials: N
authors.lastname: Segnan
mesh_terms: D000328:Adult; D000368:Aged; D003198:Computer Simulation / Q000706:statistics & numerical data; D015986:Confounding Factors, Epidemiologic; D005260:Female; D006801:Humans; D008327:Mammography / Q000706:statistics & numerical data; D008875:Middle Aged; D015233:Models, Statistical; D008962:Models, Theoretical; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data*; D013997:Time Factors; D016312:Treatment Refusal