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Adjusting for covariates in non‐inferiority studies with margins defined as risk differences
Author(s) -
Mohamed Khadeeja,
Embleton Andrew,
Cuffe Robert L.
Publication year - 2011
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.520
Subject(s) - covariate , statistics , logit , econometrics , logistic regression , scale (ratio) , margin (machine learning) , odds ratio , negative binomial distribution , sample size determination , mathematics , demography , computer science , geography , machine learning , cartography , sociology , poisson distribution
Adjusting for covariates makes efficient use of data and can improve the precision of study results or even reduce sample sizes. There is no easy way to adjust for covariates in a non‐inferiority study for which the margin is defined as a risk difference. Adjustment is straightforward on the logit scale, but reviews of clinical studies suggest that the analysis is more often conducted on the more interpretable risk‐difference scale. We examined four methods that allow for adjustment on the risk‐difference scale: stratified analysis with Cochran–Mantel–Haenszel (CMH) weights, binomial regression with an identity link, the use of a Taylor approximation to convert results from the logit to the risk‐difference scale and converting the risk‐difference margin to the odds‐ratio scale. These methods were compared using simulated data based on trials in HIV. We found that the CMH had the best trade‐off between increased efficiency in the presence of predictive covariates and problems in analysis at extreme response rates. These results were shared with regulatory agencies in Europe and the USA, and the advice received is described. Copyright © 2011 John Wiley & Sons, Ltd.