@article {doi:10.1177/00811750221112398,
	title = {Bayesian Multistate Life Table Methods for Large and Complex State Spaces: Development and Illustration of a New Method},
	journal = {Sociological Methodology},
	volume = {52},
	year = {2022},
	pages = {254-286},
	abstract = {Multistate life table methods are an important tool for producing easily understood measures of population health. Most contemporary uses of these methods involve sample data, thus requiring techniques for capturing uncertainty in estimates. In recent decades, several methods have been developed to do so. Among these methods, the Bayesian approach proposed by Lynch and Brown has several unique advantages. However, the approach is limited to estimating years to be spent in only two living states, such as {\textquotedblleft}healthy{\textquotedblright} and {\textquotedblleft}unhealthy.{\textquotedblright} In this article, the authors extend this method to allow for large state spaces with {\textquotedblleft}quasi-absorbing{\textquotedblright} states. The authors illustrate the new method and show its advantages using data from the Health and Retirement Study to investigate U.S. regional differences in years of remaining life to be spent with diabetes, chronic conditions, and disabilities. The method works well and yields rich output for reporting and subsequent analyses. The expanded method also should facilitate the use of multistate life tables to address a wider array of social science research questions.},
	keywords = {Bayesian estimation, discrete-time Markov processes, multistate life table},
	doi = {10.1177/00811750221112398},
	url = { },
	author = {Scott M. Lynch and Emma Zang}
}
