Longitudinal Models for the Analysis of Health Care Costs and Outcomes: Application to Cost-Effectiveness Analysis

• Activity Number: 158
• Type: Invited
• Date/Time: Tuesday, August 13, 2002 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section*
• Author(s): Joseph Gardiner*+ and Elena Polverejan
• Affiliation(s): Michigan State University and Pfizer, Inc.
• Keywords: Martingale ; Mixed Model ; Estimating Equation

Abstract

Markov models provide a natural setting to describe the evolution of event histories of patients through different health states. They are used in clinical decision analyses and cost-effectiveness analyses. Using this longitudinal framework, we describe stochastic models that reflect the experience of patients in sustained and changing states of health. Costs are engendered in random amounts at random points in time during the course of a health care intervention. By compiling these expenditure streams at the individual level into costs per unit time of sojourn in a health state, and in transition between health states, we estimate the distribution of present value of all expenditures and summary statistics such as mean and median costs. We then estimate health outcome measures such as life expectancy, median survival and survival rates--all discounted where appropriate at a constant rate and adjusted for quality of life.

For cost-effectiveness analyses, these methods yield estimates of intervention effects adjusted for covariates that might have impact on measures of cost and effectiveness and provide a basis for inference on cost-effectiveness ratios and net benefit measures.