Flexible transition timing in discrete-time multistate life tables using Markov chains with rewards
Bibliographic Data
| ID | 4269252 |
|---|---|
| Authors | David C Schneider (0000-0003-1615-2482, Max Planck Institute for Demographic Research), Mikko Myrskylä (0000-0003-4995-027X, Max Planck Institute for Demographic Research), Alyson Van Raalte (0000-0002-0676-8921, Max Planck Institute for Demographic Research) |
| Year | 2024 |
| Volume | 78 |
| Issue | 3 |
| Pages | 413-427 |
| Publication date | 2024-09-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Population Studies (JOURNAL) |
| Journal identifiers | ISSN: 0032-4728 • E-ISSN: 1477-4747 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00324728.2023.2176535 |
| PMID | 36880359 |
| OpenAlex | W4323347292 |
| Language | EN |
| Citations received | 2 |
| References cited | 48 |
Discrete-time multistate life tables are attractive because they are easier to understand and apply in comparison with their continuous-time counterparts. While such models are based on a discrete time grid, it is often useful to calculate derived magnitudes (e.g. state occupation times), under assumptions which posit that transitions take place at other times, such as mid-period. Unfortunately, currently available models allow very few choices about transition timing. We propose the use of Markov chains with rewards as a general way of incorporating information on the timing of transitions into the model. We illustrate the usefulness of rewards-based multistate life tables by estimating working life expectancies using different retirement transition timings. We also demonstrate that for the single-state case, the rewards approach matches traditional life-table methods exactly. Finally, we provide code to replicate all results from the paper plus R and Stata packages for general use of the method proposed
Algorithm · Code (set theory) · Data mining · Discrete time and continuous time · Grid · Machine learning · Markov chain · Markov model · Markov process · Replicate · State (computer science) · Statistics · Table (database) · Transition (genetics) · Computer Science · demographic modeling and climate adaptation · Insurance, Mortality, Demography, Risk Management · Mathematics · Retirement, Disability, and Employment
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| Unique citing works | 2 |
|---|---|
| Citations per year | 0,67 |
| Citation span | 2023 - 2025 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |