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Multistate life-tables and regression models.

R D Gill

    Mathematical Population Studies
    |January 1, 1992
    PubMed
    Summary
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    This study surveys modern statistical methods for event history analysis, focusing on multi-state life-table models in demography. It highlights the integration of partial likelihood and nonparametric maximum likelihood for complex models including semi-Markov and frailty analyses.

    Area of Science:

    • Demography
    • Biostatistics
    • Survival Analysis

    Background:

    • Event history analysis is crucial for understanding demographic transitions.
    • Multi-state life-tables model complex life course events.
    • Statistical methods are evolving for these analyses.

    Purpose of the Study:

    • To survey modern statistical techniques in event history analysis.
    • To focus on multi-state life-table studies in demography.
    • To emphasize the interplay between partial likelihood and nonparametric maximum likelihood methods.

    Main Methods:

    • Review of partial likelihood methods.
    • Review of nonparametric maximum likelihood methods.
    • Application to semi-Markov models, repeated spells, and frailty models.
    Keywords:
    Demographic AnalysisFamily And HouseholdFamily ResearchLife CycleLife Table MethodMathematical ModelModels, TheoreticalProbabilityResearch MethodologyStatistical StudiesStudiesWorld

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    Main Results:

    • Demonstration of the interplay between partial likelihood and nonparametric maximum likelihood.
    • Application of these methods to semi-Markov models and repeated spells.
    • Integration of these methods within frailty models for unobservable heterogeneity.

    Conclusions:

    • Modern statistical techniques offer powerful tools for demographic event history analysis.
    • The combination of partial likelihood and nonparametric maximum likelihood is key for complex models.
    • These methods advance the analysis of unobservable heterogeneity in life-table studies.