Commentary: Fine-tuning Life-espectancy Calculations Using Markov Processes

Fa, Sonnenberg; Wong, Jb

doi:10.1177/0272989x9301300215

Cited by 21 publications

(2 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar argument had also been made, implicitly or explicitly and in somewhat different contexts, by(Guilkey & Rindfuss, 1987;Sonnenberg & Wong, 1993). 2 The continuous-time MSLT equivalent to this is the linear l(x) ("linear integration") assumption(Schoen, 1988, p. 78).…”

supporting

confidence: 52%

Flexible transition timing in discrete-time multistate life tables using Markov chains with rewards

Schneider¹,

Myrskylä²,

Raalte³

2021

View full text Add to dashboard Cite

Discrete-time multistate life tables are attractive because they are easier to understand and apply in comparison to their continuous-time counterparts. While such models are based on a discrete time grid, it is often useful to calculate derived magnitudes, like state occupation times, under assumptions that posit that transitions take place at other times, such as mid-period. Unfortunately, currently available models allow only a very limited set of choices about transition timing. We propose to utilize Markov chains with rewards as an intuitive and general way of modelling the timing of transitions. Combining existing discrete-time models with the rewards methodology results in an estimation strategy that features easy parameter estimation, flexible transition timing, and little theoretical overhead. We illustrate the usefulness of rewardsbased multistate life tables with SHARE data for the estimation of working life expectancy using different retirement transition timings. We also demonstrate that, for the single-state case, the rewards-based multistate life tables match traditional life table methods exactly. We provide code to replicate all results of the paper, as well as R and Stata packages for general use of the method proposed.

show abstract

supporting

confidence: 52%

Flexible transition timing in discrete-time multistate life tables using Markov chains with rewards

Schneider¹,

Myrskylä²,

Raalte³

2021

View full text Add to dashboard Cite

show abstract

“…This 0-1 vector inherits the block structure of the vectorñ in ( 6) and the age-stage matri-cesŨ, etc. Also define a vector ¬h that contains the logical complement of the entries of h; i.e., Sonnenberg and Wong 1993) Demographers solve this problem when calculating mortality rates from counts of individuals and numbers of deaths by defining a fraction of year lived by those who die. The fraction is often denoted by a x in life table presentations; a x is often taken to be 0.5, indicating that individuals that die are alive, on average, for one-half of an age interval.…”

Section: Examples In the Colorectal Cancer Modelmentioning

confidence: 99%

Healthy longevity from incidence-based models: More kinds of health than stars in the sky

Caswell¹,

Daalen²

2021

DemRes

View full text Add to dashboard Cite

BACKGROUNDHealthy longevity (HL) is an important measure of the prospects for quality of life in ageing societies. Incidence-based (cf. prevalence-based) models describe transitions among age classes and health stages. Despite the probabilistic nature of those transitions, analyses of healthy longevity have focused persistently on means ("health expectancy"), neglecting variances and higher moments.

show abstract