說明 
136 p 
附註 
Source: Dissertation Abstracts International, Volume: 5809, Section: B, page: 4555 

Director: David W. Hosmer 

Thesis (Ph.D.)University of Massachusetts Amherst, 1997 

A commonly used method for confounder selection is to determine the percent difference between the crude and adjusted odds ratio of the covariate of interest, and to include the adjusting variable if the difference is greater than 1015%. However, in logistic regression the crude and adjusted odds ratio may be different even in the absence of confounding, a phenomenon called modification 

This research shows through simulations that the change in odds ratio rule often leads to incorrect inclusion or exclusion of a covariate. Alternative ways for covariate selection are suggested that take confounding and modification as well as bias and variability of the estimated odds ratio into account 

In addition, this research investigates the theoretical performance of the logistic regression model in terms of model fit by examining the discrepancy between misspecified logistic and true models using the KullbackLeibler discrepancy (KLIC) and the Pearson $\chi\sp2.$ It is found that even though the discrepancy measures increase with the degree of model misspecification, large increases in misspecification often result in small changes in the discrepancy measures. The results suggest that statistics measuring lack of fit are large only if the misspecification is severe 

The use of an empirical estimator of the KullbackLeibler discrepancy based on nonparametric kernel estimation is examined. Its performance in approximating the KLIC is compared to the performance of the empirical Pearson $\chi\sp2$ statistic and the HosmerLemeshow statistic as estimators of the true Pearson $\chi\sp2$ discrepancy. It is shown that the empirical estimator of the KLIC approximates the true discrepancy more closely than the other two statistics, but that it can only distinguish between highly different levels of model fit 

School code: 0118 

DDC 
Host Item 
Dissertation Abstracts International 5809B

主題 
Biology, Biostatistics


Health Sciences, Public Health


Statistics


0308


0573


0463

Alt Author 
University of Massachusetts Amherst

