Max Planck Institute for Demographic Research.
University of Helsinki.
Popul Stud (Camb). 2024 Nov;78(3):413-427. doi: 10.1080/00324728.2023.2176535. Epub 2023 Mar 7.
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.
离散时间多状态生命表很有吸引力，因为它们比连续时间生命表更容易理解和应用。虽然这些模型基于离散时间网格，但在假设转移发生在其他时间（例如中期）的情况下，计算派生量（例如状态占用时间）通常很有用。不幸的是，目前可用的模型对转移时间的选择非常有限。我们提出使用带奖励的马尔可夫链作为将转移时间信息纳入模型的一般方法。我们通过使用不同的退休转移时间来估计工作预期寿命，说明了基于奖励的多状态生命表的有用性。我们还证明，对于单状态情况，奖励方法与传统生命表方法完全匹配。最后，我们提供了复制本文所有结果的代码，以及 R 和 Stata 包，以供广泛使用所提出的方法。
