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


CONCLUSIONS
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.
Gould
A Lawrence
AL
Merck Research Laboratories, Rahway, NJ, USA. goulda@merck.com
Koglin
Joerg
J
Bain
Raymond P
RP
Pinto
Cathy-Anne
CA
Mitchel
Yale B
YB
Pasternak
Richard C
RC
Sapre
Aditi
A
D001499:Bayes Theorem; D002339:Carotid Arteries / Q000187:drug effects* / Q000473:pathology; D002340:Carotid Artery Diseases / Q000188:drug therapy* / Q000503:physiopathology; D018450:Disease Progression; D006801:Humans; D000960:Hypolipidemic Agents / Q000627:therapeutic use*; D015233:Models, Statistical; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic; D012106:Research; D012107:Research Design; D012307:Risk Factors; D018401:Sample Size; D017539:Tunica Intima / Q000187:drug effects*; D017540:Tunica Media / Q000187:drug effects*
D000960:Hypolipidemic Agents