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The bootstrap approach to the multistate life table method using Stata: Does accounting for complex survey designs matter?

Author

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  • Nader Mehri

    (University of North Carolina at Chapel Hill)

Abstract

Objective: I aim to develop a Stata program that estimates multistate life table quantities and their confidence intervals while controlling for covariates of interest, as well as adjusting for complex survey designs. Using the Health and Retirement Study (HRS) (2000–2016), I use the new program to estimate US females’ total, healthy, and unhealthy life expectancies and their intervals by race/ethnicity at age 52 (the youngest age in the sample), while adjusting for education. Methods: Using the nonparametric bootstrap technique (with replacement), the present study offers and validates an age-inhomogeneous first-order Markov chain multistate life table program. The current proposed Stata program is the maximum likelihood version of Lynch and Brown’s Bayesian approach to the multistate life table method, which has been developed in R. I use the estimates from the Bayesian approach to validate the estimates from the unweighted bootstrap approach. I also account for the HRS complex survey design using the HRS baseline survey design indicators (clustering, strata, and sample weights). I utilize the estimates from the unweighted and weighted bootstrap models to evaluate the extent to which ignoring the HRS complex survey design alters the estimates. Results: The health expectancy estimates obtained from the unweighted bootstrap approach are consistent with estimates from the Bayesian approach, which ignores complex survey designs. This indicates that the bootstrap approach developed in the current paper is valid. Also, the results show that ignoring the HRS complex survey design does not meaningfully alter the estimates. Contribution: The paper contributes to the multistate life table methods literature by providing a flexible, valid, and user-friendly program to estimate multistate life table quantities and their variabilities in Stata.

Suggested Citation

  • Nader Mehri, 2022. "The bootstrap approach to the multistate life table method using Stata: Does accounting for complex survey designs matter?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(23), pages 695-726.
    • Handle: RePEc:dem:demres:v:47:y:2022:i:23
    DOI: 10.4054/DemRes.2022.47.23
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    References listed on IDEAS

    as
    1. Jeronimo Oliveira Muniz, 2020. "Multistate life tables using Stata," Stata Journal, StataCorp LP, vol. 20(3), pages 721-745, September.
    2. Stanislav Kolenikov, 2010. "Resampling variance estimation for complex survey data," Stata Journal, StataCorp LP, vol. 10(2), pages 165-199, June.
    3. Scott Lynch & J. Brown, 2010. "Obtaining multistate life table distributions for highly refined subpopulations from cross-sectional data: A bayesian extension of sullivan’s method," Demography, Springer;Population Association of America (PAA), vol. 47(4), pages 1053-1077, November.
    4. Gary Solon & Steven J. Haider & Jeffrey M. Wooldridge, 2015. "What Are We Weighting For?," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 301-316.
    5. Jessica S West & Scott M Lynch & Deborah S Carr, 2021. "Demographic and Socioeconomic Disparities in Life Expectancy With Hearing Impairment in the United States [Prevalence of hearing loss and differences by demographic characteristics among US adults:," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 76(5), pages 944-955.
    6. Liming Cai & Eileen Crimmins & Yasuhiko Saito & Mark D. Hayward & James Lubitz & Aaron Hagedorn, 2010. "Estimation of multi-state life table functions and their variability from complex survey data using the SPACE Program," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 22(6), pages 129-158.
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    More about this item

    Keywords

    multistate life tables; health expectancies; Stata; Health and Retirement Study (HRS); race/ethnicity; complex survey;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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