JSM 2004 - Toronto

Abstract #301889

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Activity Number: 397
Type: Topic Contributed
Date/Time: Thursday, August 12, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301889
Title: Modeling Count Data in Healthcare Utilization with Endogeneity
Author(s): Zhen Chen*+
Companies: University of Pennsylvania
Address: 423 Guardian Dr., Philadelphia, PA, 19104,
Keywords: negative binomial ; simultaneous equations ; Poisson models ; random effects ; MCMC ; moral hazads
Abstract:

In modeling count data in healthcare utilization, it is usually the case that the regressor of primary interest is endogenously determined. Moreover, restrictions in data collection process may result in missing data problem. For instance, patients who seek no treatments have missingness in copayment variable if copayment information is obtained from pharmacy records. We propose a joint model that uses a truncated negative binomial regression for the count outcome given the endogenous regressor, and a proportional odds model for the categorized endogenous regressor given other exogenous variables. We implement an MCMC procedure to estimate model parameters and apply the method to a study that investigates copayment effects on number of drug prescriptions in patients with depression disorders.

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Revised March 2004