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Missing data problems persist in many scientific investigations. Although various strategies for analyzing missing data have been proposed, they are mainly limited to data on continuous measurements. In this paper, we focus on implementing some of the available strategies to analyze item response data. In particular, we investigate the effects of popular missing data methods on various missing data mechanisms. We examine large sample behaviors of estimators in a simulation study that evaluates and compares their performance. We use data from a quality of life study with lung cancer patients to illustrate the utility of these methods. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Ordinal response data are commonly observed in health and medical investigations that include several items. The primary goal in the modelling of item response data is to find a unique measurement of the person's abilities and of the item difficulties that satisfies the properties of the fundamental measurement. One such analytic method in item response theory is the Rasch measurement, which is a way to convert ordinal observations into linear measures. Current estimation strategies assume the independence of the Rasch model parameters. In this paper, based on the conditional maximum likelihood, we implemented a simultaneous estimation method that can compare the Rasch parameters more efficiently. We also obtained the asymptotic properties of these estimators and developed the conditional likelihood ratio test for the goodness‐of‐fit of the model. Simulation studies were used to demonstrate the improved performance of our estimators as compared to that of currently used conditional method known as the CON procedure. We conclude that our estimation method outperforms CON in both model fit and the precision of the Rasch estimators. Copyright © 2002 John Wiley & Sons, Ltd.

Continuity of care is a concept that is frequently used to measure patient satisfaction and other benefits provided by the health care system. Although several measures of continuity of care are used, most are built on unrealistic assumptions about random visits to providers and do not utilize all available information. In this paper we advocate stochastic processes to model continuity of care, and we propose a new measure of provider continuity based on the multiplicative intensity model. Using data on hypertensive patients in the province of Alberta, Canada, we demonstrate the utility of our method that incorporates ways to accommodate covariate information. Compared to the currently popular method, the proposed method in this paper is shown to assess both concentration of care aspects and other visit‐based characteristics of the care being provided. Copyright © 2002 John Wiley & Sons, Ltd.

Continuity of care is a concept that is frequently used to measure patient satisfaction and other benefits provided by the health care system. Although several measures of continuity of care are used, most are built on unrealistic assumptions about random visits to providers and do not utilize all available information. In this paper we advocate stochastic processes to model continuity of care, and we propose a new measure of provider continuity based on the multiplicative intensity model. Using data on hypertensive patients in the province of Alberta, Canada, we demonstrate the utility of our method that incorporates ways to accommodate covariate information. Compared to the currently popular method, the proposed method in this paper is shown to assess both concentration of care aspects and other visit-based characteristics of the care being provided. Copyright © 2002 John Wiley & Sons, Ltd.

Ordinal response data are commonly observed in health and medical investigations that include several items. The primary goal in the modelling of item response data is to find a unique measurement of the person's abilities and of the item difficulties that satisfies the properties of the fundamental measurement. One such analytic method in item response theory is the Rasch measurement, which is a way to convert ordinal observations into linear measures. Current estimation strategies assume the independence of the Rasch model parameters. In this paper, based on the conditional maximum likelihood, we implemented a simultaneous estimation method that can compare the Rasch parameters more efficiently. We also obtained the asymptotic properties of these estimators and developed the conditional likelihood ratio test for the goodness-of-fit of the model. Simulation studies were used to demonstrate the improved performance of our estimators as compared to that of currently used conditional method known as the CON procedure. We conclude that our estimation method outperforms CON in both model fit and the precision of the Rasch estimators. Copyright © 2002 John Wiley & Sons, Ltd.