Power and sample size calculations for generalized regression models with covariate measurement error.
Covariate measurement error is often a feature of scientific data used for regression modelling. The consequences of such errors include a loss of power of tests of significance for the regression parameters corresponding to the true covariates. Power and sample size calculations that ignore covariate measurement error tend to overestimate power and underestimate the actual sample size required to achieve a desired power. In this paper we derive a novel measurement error corrected power function for generalized linear models using a generalized score test based on quasi-likelihood methods. Our power function is flexible in that it is adaptable to designs with a discrete or continuous scalar covariate (exposure) that can be measured with or without error, allows for additional confounding variables and applies to a broad class of generalized regression and measurement error models. A program is described that provides sample size or power for a continuous exposure with a normal measurement error model and a single normal confounder variable in logistic regression. We demonstrate the improved properties of our power calculations with simulations and numerical studies. An example is given from an ongoing study of cancer and exposure to arsenic as measured by toenail concentrations and tap water samples.
Tosteson
Tor D
TD
Dartmouth Medical School, Lebanon NH 03756, USA. tor.tosteson@dartmouth.edu
Buzas
Jeffrey S
JS
Demidenko
Eugene
E
Karagas
Margaret
M
D001151:Arsenic / Q000009:adverse effects; D015982:Bias; D015340:Epidemiologic Research Design; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D016015:Logistic Models; D009262:Nails / Q000737:chemistry; D018401:Sample Size; D012878:Skin Neoplasms / Q000139:chemically induced; D001749:Urinary Bladder Neoplasms / Q000139:chemically induced; D014881:Water Supply
D001151:Arsenic