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Effects of sources of variability on sample sizes required for RCTs, applied to trials of lipid-altering therapies on carotid artery intima-media thickness

Merck Research Laboratories, Rahway, NJ, USA, goulda{at}merck.com

Joerg Koglin

Merck Research Laboratories, Rahway, NJ, USA

Raymond P Bain

Merck Research Laboratories, Rahway, NJ, USA

Cathy-Anne Pinto

Merck Research Laboratories, Rahway, NJ, USA

Yale B Mitchel

Merck Research Laboratories, Rahway, NJ, USA

Richard C Pasternak

Merck Research Laboratories, Rahway, NJ, USA

Aditi Sapre

Merck Research Laboratories, Rahway, NJ, USA

Objective Studies measuring progression of carotid artery intima-media thickness (cIMT) have been used to estimate the effect of lipid-modifying therapies cardiovascular event risk. The likelihood that future cIMT clinical trials will detect a true treatment effect is estimated by leveraging results from prior studies. The present analyses assess the impact of between- and within-study variability based on currently published data from prior clinical studies on the likelihood that ongoing or future cIMT trials will detect the true treatment effect of lipid-modifying therapies.

Methods Published data from six contemporary cIMT studies (ASAP, ARBITER 2, RADIANCE 1, RADIANCE 2, ENHANCE, and METEOR) including data from a total of 3563 patients were examined. Bayesian and frequentist methods were used to assess the impact of between study variability on the likelihood of detecting true treatment effects on 1-year cIMT progression/regression and to provide a sample size estimate that would specifically compensate for the effect of between-study variability.

Results In addition to the well-described within-study variability, there is considerable between-study variability associated with the measurement of annualized change in cIMT. Accounting for the additional between-study variability decreases the power for existing study designs. In order to account for the added between-study variability, it is likely that future cIMT studies would require a large increase in sample size in order to provide substantial probability (90%) to have 90% power of detecting a true treatment effect.

Limitation Analyses are based on study level data. Future meta-analyses incorporating patient-level data would be useful for confirmation.

Conclusion Due to substantial within- and between-study variability in the measure of 1-year change of cIMT, as well as uncertainty about progression rates in contemporary populations, future study designs evaluating the effect of new lipid-modifying therapies on atherosclerotic disease progression are likely to be challenged by large sample sizes in order to demonstrate a true treatment effect.

Clinical Trials, Vol. 6, No. 4, 305-319 (2009)
DOI: 10.1177/1740774509339977


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