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Estimation of life expectancies using continuous-time multi-state models
Ardo van den Hout1, Mei Sum Chan2, Fiona Matthews3
1Department of Statistical Science, University College London Gower Street, London WC1E 6BT, UK.
Computer Methods and Programs in Biomedicine
|August 17, 2019
Summary
This study introduces new methods and R software (elect package) for calculating state-specific life expectancies from multi-state health models. These calculations help researchers better understand and present health-related processes.
Area of Science:
- Biostatistics
- Health Economics
- Epidemiology
Background:
- Multi-state models are increasingly used for health-related stochastic processes.
- Estimating life expectancies is crucial for understanding health outcomes.
- Existing methods for life expectancy calculation in complex models are limited.
Purpose of the Study:
- To introduce novel methods for computing state-specific and marginal life expectancies.
- To present new software (R package 'elect') for these computations.
- To facilitate the analysis and communication of health-related processes.
Main Methods:
- Extending the definition of state-specific life expectancy from standard survival analysis.
- Utilizing estimated parameters from fitted multi-state models.
- Employing numerical integration techniques for computation.
- Developing user-friendly functions within the R package 'elect'.
Main Results:
- The 'elect' R package enables the estimation and exploration of life expectancies.
- Functions are provided for data analysis, expectancy calculation, and result presentation.
- Illustrations demonstrate the practical application of the methods.
Conclusions:
- State-specific life expectancies offer a clear way to represent health-related processes.
- The 'elect' package simplifies the computation and presentation of life expectancy findings.
- This work enhances researchers' ability to analyze and communicate complex health data.
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