Active life expectancy from annual follow-up data with missing responses

Active life expectancy (ALE) at a given age is defined as the expected rema ining years free of disability. In this study three categories of health st atus are defined according to the ability to perform activities of daily li ving independently. Several studies have used increment-decrement life tabl es to estimate ALE, without error analysis, from only a baseline and one fo llow-up interview. The present work conducts an individual-level covariate analysis using a three-state Markov chain model fur multiple follow-up data . Using a logistic link, the model estimates single-year transition probabi lities among states of health, accounting for missing interviews. This appr oach has the advantages of smoothing subsequent estimates and increased pow er by using all follow-ups. We compute ALE and total life expectancy from t hese estimated single-year transition probabilities. Variance estimates are computed using the delta method. Data from the Iowa Established Population for the Epidemiologic Study of the Elderly are used to test the effects of smoking on ALE on all 5-year age groups past 65 years, controlling for sex and education.