Software for multistate analysis

Willekens, Frans; Putter, Hein

doi:10.4054/demres.2014.31.14

Cited by 19 publications

(18 citation statements)

References 84 publications

(82 reference statements)

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“…Those below 1× poverty have the highest probability of exiting at each age, but the sharpest decline with age (2). Although Willekens [ 14 ] did not investigate age-specific transition rates, the annual rates are roughly similar in magnitude for exiting poverty and above poverty, and for exiting 2× poverty and above 2× poverty. Our transition rates are also consistent with poverty entry and exit rates mentioned in [ 4 ] (although our rates are age-specific).…”

Section: Discussionmentioning

confidence: 99%

“…Researchers might look towards [ 36 ] or [ 37 ] which are two distinct ways to calculate Markov transition probabilities that can incorporate a range of data complexities. Willekens’ review [ 14 ] is also a good resource as to the vast array of statistical packages in R that can be used to estimate transition probabilities for demographic multi-state models. Logistic regression models are often used in the poverty context to find determinants of poverty [ 38 ].…”

Section: Methods: Empirical Datamentioning

confidence: 99%

“…Matrix age-by-stage models have been used in many contexts (for instance to analyze plants, such as a perennial shrub [ 10 ], animals, for instance whales [ 11 ], humans [ 12 ], and in epidimiological analysis, for instance to analyze rubella [ 13 ]) and are useful for connecting individual stochasticity in life path to overall cohort dynamics. Other methods that use the combination of age and stage to predict demographic fates, include multistate life table approaches (as reviewed by Willekens [ 14 ] or classic increment-decrement life tables techniques [ 15 ]) can address similar questions. In deference to work on multi-state life table analysis, we use the human demography convention and use the term ‘state’ rather than the more general ecology term ‘stage’.…”

Section: Introductionmentioning

confidence: 99%

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Poverty dynamics, poverty thresholds and mortality: An age-stage Markovian model

et al. 2018

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Recent studies have examined the risk of poverty throughout the life course, but few have considered how transitioning in and out of poverty shape the dynamic heterogeneity and mortality disparities of a cohort at each age. Here we use state-by-age modeling to capture individual heterogeneity in crossing one of three different poverty thresholds (defined as 1×, 2× or 3× the “official” poverty threshold) at each age. We examine age-specific state structure, the remaining life expectancy, its variance, and cohort simulations for those above and below each threshold. Survival and transitioning probabilities are statistically estimated by regression analyses of data from the Health and Retirement Survey RAND data-set, and the National Longitudinal Survey of Youth. Using the results of these regression analyses, we parameterize discrete state, discrete age matrix models. We found that individuals above all three thresholds have higher annual survival than those in poverty, especially for mid-ages to about age 80. The advantage is greatest when we classify individuals based on 1× the “official” poverty threshold. The greatest discrepancy in average remaining life expectancy and its variance between those above and in poverty occurs at mid-ages for all three thresholds. And fewer individuals are in poverty between ages 40-60 for all three thresholds. Our findings are consistent with results based on other data sets, but also suggest that dynamic heterogeneity in poverty and the transience of the poverty state is associated with income-related mortality disparities (less transience, especially of those above poverty, more disparities). This paper applies the approach of age-by-stage matrix models to human demography and individual poverty dynamics. In so doing we extend the literature on individual poverty dynamics across the life course.

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Section: Discussionmentioning

confidence: 99%

Section: Methods: Empirical Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Poverty dynamics, poverty thresholds and mortality: An age-stage Markovian model

et al. 2018

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“…There are many different statistical approaches to estimating period age-specific transitions rates from longitudinal survey data (Willekens and Putter, 2014). One commonly used approach is called Interpolated Markov Chains (IMaCh) and is available as a software (Lièvre et al, 2003;Brouard and Lièvre, 2009).…”

Section: Statistical Approaches To Estimating Multistate Life Tablesmentioning

confidence: 99%

Multistate Transition Models in Demography

Guillot

2015

International Encyclopedia of the Social &Amp; Behavioral Sciences

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“…The oldest application dates from the early 20th century, when DuPasquier (1912) investigated transitions between two conditions: healthy and disabled. Since then, much work has been done to advance and implement multistate models (Rogers 1973(Rogers , 1975Ledent 1980;Schoen 1988a,b;Palloni 2001), with applications to marital status (Schoen and Land 1979), labor force participation (Smith 1982;Willekens 1980), interregional migration (Rogers 1975), population projections by educational attainment (Lutz and Goujon 2001), active and disabled (Land, Guralnik, and Blazer 1994), and happy life expectancies (Yang and Walij 2010). In addition, a plethora of computer programs have been written to estimate multistate life tables (Willekens and Putter 2014), but most of them are not "flexible enough to handle a very broad class of applications or to implement alternative solutions" (Palloni 2001, 272).…”

Section: Introductionmentioning

confidence: 99%

Multistate life tables using Stata

Muniz

2020

The Stata Journal

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The mslt command calculates the functions of a multistate life table and plots a graph of conditional and unconditional life expectancies by time. The command provides linear and exponential solutions to estimate the number of individuals, transitions, probabilities, person-years, and years of life in a given cohort and state of occupancy. The input data are time-specific transition rates (or survivorship proportions) between nonabsorbing and at most one absorbing state. In addition to the mean age at transfer between states, mslt calculates the following summary measures: the mean age, the probability of dying, the average duration, and the proportion of life spent in a specific state.

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Software for multistate analysis

Cited by 19 publications

References 84 publications

Poverty dynamics, poverty thresholds and mortality: An age-stage Markovian model

Poverty dynamics, poverty thresholds and mortality: An age-stage Markovian model

Multistate Transition Models in Demography

Multistate life tables using Stata

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