STATISTICAL ANALYSIS OF REGRESSION MODELS WITH COVARIATES MEASURED WITH ERROR (DAIRY CATTLE, NURSING HOMES).
Contributors: University Of Guelph (Canada)
Published: 1996
Number of pages: 271
Subjects: Statistics.; Biology, Biostatistics.; Health Sciences, Health Care Management.; Health Sciences, Rehabilitation and Therapy.
Description: Computer simulations and data analysis were used to investigate the sampling properties of regression estimates when covariates were measured with error in linear, logistic, and survival models. The simulations were mainly designed to examine the effects of measurement error on the estimates in linear and logistic regressions. Techniques of parameter estimation, developed in this study, were illustrated on two data sets. The first, describing mastitis in dairy cattle in Ontario, was used in logistic models. The second set was used in survival models, and described the length of stay at nursing homes in California.

In simple logistic regressions four techniques to reduce the bias in parameter estimates were examined. Based on the simulations, measurement errors in covariates substantially attentuated parameter estimates. All four techniques reduced the bias in the estimates when the regression coefficient was medium. However, when the regression coefficient was large, only one produced satisfactory bias reduction in parameter estimates.

The effects of measurement errors in multiple logistic models were contrasted with those in linear models. Simulations revealed that the characteristics of their effects were slightly different. In the logistic regressions, measurement error affected not only the corresponding estimates, but also the estimate of other covariates, although they were independent from the covariates measured with error. Additionally, the resulting estimates of the parameters of accurately measured covariates which were independent from the erroneous covariates were attenuated.

A proposed combined technique produced better parameter estimates when compared to the use of a single method. This technique requires a validation study for the covariates which are measured with error. When validated data are not available, the instrumental variable technique is recommended.

In survival models, parameters of the models were estimated by the least squares method and the maximum likelihood technique. Both approaches produced similar estimates and comparable standard errors when applied to the California nursing home data.
Notes: Source: Dissertation Abstracts International, Volume: 57-12, Section: B, page: 7593.

Thesis (PH.D.)--UNIVERSITY OF GUELPH (CANADA), 1996.

School code: 0081.