LEADER 02786cmm 2200325 a 450

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008 030314s2002 eng d

020 $a0493695850

035 $a00106498

040 $aUnM$cUnM

090 $aE-BOOK/378.242Pit/2002/////UM009589

100 1 $aLi, Wei.$3238498

245 10$aResampling approach for estimating prediction error and for adjusting logistic regression models for covariate measurement error$h[electronic resource] /$cWei Li.

260 $aAnn Arbor :$bProQuest Dissertations & Theses,$c2002.

300 $a78 p.

500 $aSource: Dissertation Abstracts International, Volume: 63-05, Section: B, page: 2146.

500 $aAdvisers: Sati Mazumdar; Vincent C. Arena.

502 $aThesis (Ph.D.)--University of Pittsburgh, 2002.

520 $aMethods based on the resampling approach are proposed to address two issues related to prediction modeling: estimation of prediction error and adjustment for covariance measurement error.

520 $aRepartitioning k-fold cross-validation is proposed to enhance the estimation of the prediction error of classification models. Compared to cross-validation, the traditional method of choice, reduces the variability of estimated prediction error. In addition, it provides an empirical distribution of prediction error rather than a single estimate unaccompanied by an estimate of standard error for the point estimate. SAS macros are developed for the implementation of CVKR.

520 $aBootstrap regression calibration is proposed to adjust the coefficient estimates of logistic regression models when measurement error is present in model covariates. This method can be thought of as a bootstrap-smoothed version of the popular regression calibration method (Rosner et al. AM. J. Epidemiol. 1990, 1992). These two methods are evaluated and compared with respect to prediction accuracy, something not found in previous works. Receiver Operating Characteristic (ROC) methodology was employed to measure models' prediction accuracy and the area under ROC curve (AUC) was used as the index for prediction accuracy. The methods were also evaluated with respect to the attenuation (or bias) in the estimated coefficients. Results from simulation studies showed that BRC offers consistent enhancement over the regression calibration method in terms of improving the prediction accuracy and reducing bias in estimated coefficients.

590 $aSchool code: 0178.

650 4$aBiology, Biostatistics.$3238499

650 4$aStatistics.$3140131

690 $a0308

690 $a0463

710 2 $aUniversity of Pittsburgh.$3238299

773 0 $tDissertation Abstracts International$g63-05B.

790 10$aMazumdar, Sati,$eadvisor

790 10$aArena, Vincent C.,$eadvisor

790 $a0178

791 $aPh.D.

792 $a2002

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