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Estimation of multi-state life table functions and their variability from complex survey data using the SPACE Program

Author

Listed:
  • Liming Cai

    (National Center for Health Statistics)

  • Mark D. Hayward

    (University of Texas at Austin)

  • Yasuhiko Saito

    (Nihon University)

  • James Lubitz

    (National Center for Health Statistics)

  • Aaron Hagedorn

    (University of Southern California)

  • Eileen Crimmins

    (University of Southern California)

Abstract

The multistate life table (MSLT) model is an important demographic method to document life cycle processes. In this study, we present the SPACE (Stochastic Population Analysis for Complex Events) program to estimate MSLT functions and their sampling variability. It has several advantages over other programs, including the use of microsimulation and the bootstrap method to estimate the sampling variability. Simulation enables researchers to analyze a broader array of statistics than the deterministic approach, and may be especially advantageous in investigating distributions of MSLT functions. The bootstrap method takes sample design into account to correct the potential bias in variance estimates.

Suggested Citation

  • Liming Cai & Mark D. Hayward & Yasuhiko Saito & James Lubitz & Aaron Hagedorn & Eileen Crimmins, 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.
    • Handle: RePEc:dem:demres:v:22:y:2010:i:6
    DOI: 10.4054/DemRes.2010.22.6
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    References listed on IDEAS

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    Cited by:

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    2. Isaac Sasson, 2017. "Reply to Trends in Education-Specific Life Expectancy, Data Quality, and Shifting Education Distributions: A Note on Recent Research," Demography, Springer;Population Association of America (PAA), vol. 54(3), pages 1215-1219, June.
    3. 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.
    4. Chi-Tsun Chiu, 2019. "Living arrangements and disability-free life expectancy in the United States," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-16, February.
    5. Marco Raffaella Piccarreta & Marco Bonetti & Stefano Lombardi, 2018. "Comparing models for sequence data: prediction and dissimilarities," Working Papers 113, "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), Università Commerciale Luigi Bocconi.
    6. Markus Sauerberg, 2020. "The Role of Education for Assessing Population Health: An Analysis of Healthy Life Expectancy by Educational Attainment for 16 European Countries," VID Working Papers 2005, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna.
    7. Holendro Singh Chungkham & Robin S. Högnäs & Jenny Head & Paola Zaninotto & Hugo Westerlund, 2023. "Estimating Working Life Expectancy: A Comparison of Multistate Models," SAGE Open, , vol. 13(2), pages 21582440231, May.
    8. Dustin Brown & Mark Hayward & Jennifer Montez & Robert Hummer & Chi-Tsun Chiu & Mira Hidajat, 2012. "The Significance of Education for Mortality Compression in the United States," Demography, Springer;Population Association of America (PAA), vol. 49(3), pages 819-840, August.
    9. Piccarreta, Raffaella & Bonetti, Marco, 2019. "Assessing and comparing models for sequence data by microsimulation (with Supplementary Material)," SocArXiv 3mcfp, Center for Open Science.
    10. Collin F. Payne & Luca Maria Pesando & Hans‐Peter Kohler, 2019. "Private Intergenerational Transfers, Family Structure, and Health in a sub‐Saharan African Context," Population and Development Review, The Population Council, Inc., vol. 45(1), pages 41-80, March.
    11. Zachary Zimmer & Yasuhiko Saito & Olga Theou & Clove Haviva & Kenneth Rockwood, 2021. "Education, wealth, and duration of life expected in various degrees of frailty," European Journal of Ageing, Springer, vol. 18(3), pages 393-404, September.
    12. Christian Dudel, 2021. "Expanding the Markov Chain Toolbox: Distributions of Occupation Times and Waiting Times," Sociological Methods & Research, , vol. 50(1), pages 401-428, February.
    13. Hal Caswell & Silke van Daalen, 2021. "Healthy longevity from incidence-based models: More kinds of health than stars in the sky," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(13), pages 397-452.
    14. Frans Willekens & Sabine Zinn & Matthias Leuchter, 2017. "Emigration Rates From Sample Surveys: An Application to Senegal," Demography, Springer;Population Association of America (PAA), vol. 54(6), pages 2159-2179, December.
    15. Marco Bonetti & Raffaella Piccarreta & Gaia Salford, 2013. "Parametric and Nonparametric Analysis of Life Courses: An Application to Family Formation Patterns," Demography, Springer;Population Association of America (PAA), vol. 50(3), pages 881-902, June.
    16. Jennifer Van Hook & Claire Altman, 2013. "Using Discrete-Time Event History Fertility Models to Simulate Total Fertility Rates and Other Fertility Measures," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 32(4), pages 585-610, August.
    17. Angelique Chan & Abhijit Visaria & Bina Gubhaju & Stefan Ma & Yasuhiko Saito, 2021. "Gender differences in years of remaining life by living arrangement among older Singaporeans," European Journal of Ageing, Springer, vol. 18(4), pages 453-466, December.

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    More about this item

    Keywords

    population aging; bootstrap; multistate life tables; health expectancy;
    All these keywords.

    JEL classification:

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

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