Covariate measurement error adjustment for matched case-control studies. Academic Article

abstract

• We propose a conditional scores procedure for obtaining bias-corrected estimates of log odds ratios from matched case-control data in which one or more covariates are subject to measurement error. The approach involves conditioning on sufficient statistics for the unobservable true covariates that are treated as fixed unknown parameters. For the case of Gaussian nondifferential measurement error, we derive a set of unbiased score equations that can then be solved to estimate the log odds ratio parameters of interest. The procedure successfully removes the bias in naive estimates, and standard error estimates are obtained by resampling methods. We present an example of the procedure applied to data from a matched case-control study of prostate cancer and serum hormone levels, and we compare its performance to that of regression calibration procedures.

authors

• Carroll, Raymond

published proceedings

• Biometrics

author list (cited authors)

• McShane, L. M., Midthune, D. N., Dorgan, J. F., Freedman, L. S., & Carroll, R. J.

citation count

• 15

complete list of authors

• McShane, LM||Midthune, DN||Dorgan, JF||Freedman, LS||Carroll, RJ

publication date

• March 2001

publisher

• Wiley  Publisher

published in

• Biometrics  Journal

keywords

• Bias
• Biometry
• Case-Control Studies
• Computer Simulation
• Dihydrotestosterone
• Humans
• Logistic Models
• Male
• Monte Carlo Method
• Odds Ratio
• Prostatic Neoplasms
• Risk Factors
• Testosterone

PubMed Central ID

• 11252619

Digital Object Identifier (DOI)

• 10.1111/j.0006-341x.2001.00062.x

start page

• 62

end page

• 73

volume

• 57

issue

• 1

URL

• http%3A%2F%2Fdx.doi.org%2F10.1111%2Fj.0006-341x.2001.00062.x