Accounting for response misclassification and covariate measurement error improves power and reduces bias in epidemiologic studies.
OBJECTIVE
To quantify the impact of ignoring misclassification of a response variable and measurement error in a covariate on statistical power, and to develop software for sample size and power analysis that accounts for these flaws in epidemiologic data.


METHODS
A Monte Carlo simulation-based procedure is developed to illustrate the differences in design requirements and inferences between analytic methods that properly account for misclassification and measurement error to those that do not in regression models for cross-sectional and cohort data.


RESULTS
We found that failure to account for these flaws in epidemiologic data can lead to a substantial reduction in statistical power, over 25% in some cases. The proposed method substantially reduced bias by up to a ten-fold margin compared to naive estimates obtained by ignoring misclassification and mismeasurement.


CONCLUSIONS
We recommend as routine practice that researchers account for errors in measurement of both response and covariate data when determining sample size, performing power calculations, or analyzing data from epidemiological studies.
Cheng
Dunlei
D
Institute for Health Care Research and Improvement, Dallas, TX 75206, USA. dunleic@baylorhealth.edu <dunleic@baylorhealth.edu>
Branscum
Adam J
AJ
Stamey
James D
JD
D015982:Bias; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D015203:Reproducibility of Results; D012107:Research Design / Q000592:standards; D018401:Sample Size; D015983:Selection Bias