title: Design issues and sample size when exposure measurement is inaccurate.
abstract: Measurement error often leads to biased estimates and incorrect tests in epidemiological studies. These problems can be corrected by design modifications which allow for refined statistical models, or in some situations by adjusted sample sizes to compensate a power reduction. The design options are mainly an additional replication or internal validation study. Sample size calculations for these designs are more complex, since usually there is no unique design solution to obtain a prespecified power. Thus, additionally to a power requirement, an optimal design should also fulfill the criteria of minimizing overall costs. In this review corresponding strategies and formulae are described and appraised.
authors: Institute for Medical Statistics and Documentation, Johannes-Gutenberg-University Mainz, Germany. rippin@imsd.uni-mainz.de
authors: G
authors: G
authors: Rippin
mesh_terms: D015982:Bias; D003625:Data Collection / Q000706:statistics & numerical data*; D004781:Environmental Exposure / Q000009:adverse effects / Q000706:statistics & numerical data*; D016021:Epidemiologic Studies; D006801:Humans; D008432:Mathematical Computing; D015233:Models, Statistical; D015203:Reproducibility of Results; D018401:Sample Size