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. 2010 Jan 8;5(1):e8580.
doi: 10.1371/journal.pone.0008580.

A novel method to adjust efficacy estimates for uptake of other active treatments in long-term clinical trials

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A novel method to adjust efficacy estimates for uptake of other active treatments in long-term clinical trials

John Simes et al. PLoS One. .

Erratum in

  • PLoS One. 2010;5(1). doi: 10.1371/annotation/54433693-04e0-4f30-9a99-38fe3c5bb16b

Abstract

Background: When rates of uptake of other drugs differ between treatment arms in long-term trials, the true benefit or harm of the treatment may be underestimated. Methods to allow for such contamination have often been limited by failing to preserve the randomization comparisons. In the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study, patients were randomized to fenofibrate or placebo, but during the trial many started additional drugs, particularly statins, more so in the placebo group. The effects of fenofibrate estimated by intention-to-treat were likely to have been attenuated. We aimed to quantify this effect and to develop a method for use in other long-term trials.

Methodology/principal findings: We applied efficacies of statins and other cardiovascular drugs from meta-analyses of randomized trials to adjust the effect of fenofibrate in a penalized Cox model. We assumed that future cardiovascular disease events were reduced by an average of 24% by statins, and 20% by a first other major cardiovascular drug. We applied these estimates to each patient who took these drugs for the period they were on them. We also adjusted the analysis by the rate of discontinuing fenofibrate. Among 4,900 placebo patients, average statin use was 16% over five years. Among 4,895 assigned fenofibrate, statin use was 8% and nonuse of fenofibrate was 10%. In placebo patients, use of cardiovascular drugs was 1% to 3% higher. Before adjustment, fenofibrate was associated with an 11% reduction in coronary events (coronary heart disease death or myocardial infarction) (P = 0.16) and an 11% reduction in cardiovascular disease events (P = 0.04). After adjustment, the effects of fenofibrate on coronary events and cardiovascular disease events were 16% (P = 0.06) and 15% (P = 0.008), respectively.

Conclusions/significance: This novel application of a penalized Cox model for adjustment of a trial estimate of treatment efficacy incorporates evidence-based estimates for other therapies, preserves comparisons between the randomized groups, and is applicable to other long-term trials. In the FIELD study example, the effects of fenofibrate on the risks of coronary heart disease and cardiovascular disease events were underestimated by up to one-third in the original analysis.

Trial registration: Controlled-Trials.com ISRCTN64783481.

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Conflict of interest statement

Competing Interests: Research grant awarded to University of Sydney from Laboratoire Fournier, and honoraria for invited addresses from Solvay Pharmaceuticals.

Figures

Figure 1
Figure 1. Time to discontinuing study medication or to starting other lipid-lowering treatment, by randomized group.
Figure 2
Figure 2. Effects of fenofibrate on events, with and without adjustment for use of statins and other drugs (RRR = relative risk reduction).
Figure 3
Figure 3. Effects of fenofibrate on cardiovascular events, by major subgroup.
*Adjusted for use of other cardiovascular drugs and discontinuation of fenofibrate. RRR = relative risk reduction.

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References

    1. White IR. Uses and limitations of randomization-based efficacy estimators. Stat Methods Med Res. 2005;14:327. - PubMed
    1. Rosenblum M, Jewell NP, van der Laan M, Shiboski S, Van der Straten A, et al. Analysing direct effects in randomized trials with secondary interventions: an application to human immunodeficiency virus prevention trials. J R Stat Soc. 2009;A172:443–465. - PMC - PubMed
    1. Yamaguchi T, Ohashi Y. Adjusting for differential proportions of second-line treatment in cancer clinical trials. Part I: Structural nested models and marginal structural models to test and estimate treatment arm effects. Stat Med. 2004;23:1991–2003. - PubMed
    1. Heritier SR, Gebski VJ, Keech AC. Inclusion of patients in clinical trial analysis: the intention-to-treat principle. Med J Aust. 2003;179:438–440. - PubMed
    1. Sheiner LB, Rubin DB. Intention-to-treat analysis and the goal of clinical trials. Clin Pharmacol Ther. 1995;56:6–10. - PubMed

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