Clustered Multistate Models with Observation Level Random Effects, Mover–Stayer Effects and Dynamic Covariates: Modelling Transition Intensities and Sojourn Times in a Study of Psoriatic Arthritis

Yiu, Sean; Farewell, Vernon; Tom, Brian D. M.

doi:10.1111/rssc.12235

Cited by 7 publications

(7 citation statements)

References 24 publications

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“…41 By integrating these functions over time, the multistate model estimates the probability of a given state transition over time, the probability of improvement or deterioration over time, and the average time spent in a given state (known as the mean sojourn time). 42,43 Multistate models are particularly well suited for describing dynamic systems, such as changes in a patient's health status over time, as they can account for patients experiencing multiple outcomes multiple times over the study period. 44,45 In contrast, traditional survival models describe a single outcome occurring only once.…”

Section: Discussionmentioning

confidence: 99%

Not all is lost: Functional recovery in older adults following emergency general surgery

Guttman

Tillmann²,

Nathens³

et al. 2022

J Trauma Acute Care Surg

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BACKGROUND:Although functional decline and death are common long-term outcomes among older adults following emergency general surgery (EGS), we hypothesized that patients' postdischarge function may wax and wane over time. Periods of fluctuation in function may represent opportunities to intervene to prevent further decline. Our objective was to describe the functional trajectories of older adults following EGS admission. METHODS:This was a population-based retrospective cohort study of all independent, community-dwelling older adults (age ≥65 years) in Ontario with an EGS admission (2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016). A multistate model was used to examine patients' functional trajectories over the 5 years following discharge. Patients were followed as they transitioned back and forth between functional independence, use of chronic home care (in-home assistance for personal care, homemaking, or medical care for at least 90 days), nursing home admission, and death. RESULTS:We identified 78,820 older adults with an EGS admission (mean age, 77 years; 53% female). In the 5 years following admission, 32% (n = 24,928) required new chronic home care, 21% (n = 5,249) of whom had two or more episodes of chronic home care separated by periods of independence. The average time spent in chronic home care was 11 months, and 50% (n = 12,679) of chronic home care episodes ended with a return to independence. For patients requiring chronic home care at any time, the probability of returning to independent living during the subsequent 5 years ranged from 36% to 43% annually. CONCLUSION:Not all is lost for older adults who experience functional decline following EGS admission. Half of those who require chronic home care will recover to independence, and one-third will have a durable recovery, remaining independent after 5 years. Fluctuations in function in the years following EGS may represent a unique opportunity for interventions to promote rehabilitation and recovery among older adults.

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Section: Discussionmentioning

confidence: 99%

Not all is lost: Functional recovery in older adults following emergency general surgery

Guttman

Tillmann²,

Nathens³

et al. 2022

J Trauma Acute Care Surg

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“…Direct, indirect, and total effects of other baseline covariates were investigated based on the additive hazard model. Yiu et al (2018) considered a clustered multistate model with dynamic covariates and analyzed the transition intensities and sojourn times in a study of psoriatic arthritis. These methods, however, are not adequate for the event history analysis when the past event feedbacks are associated with the longitudinal covariates.…”

Section: Introductionmentioning

confidence: 99%

Multistate analysis of multitype recurrent event and failure time data with event feedbacks in biomarkers

Pan

2021

Scandinavian J Statistics

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In this paper we propose a class of multistate models for the analysis of multitype recurrent event and failure time data when there are past event feedbacks in longitudinal biomarkers. It can well incorporate various effects, including time-dependent and time-independent effects, of different event paths or the number of occurrences of events of different types.Asymptotic unbiased estimating equations based on polynomial splines approximation are developed. The consistency and asymptotic normality of the proposed estimators are provided. Simulation studies show that the naive estimators which either ignore the past event feedback or the measurement errors are biased.Our method has a better coverage probability of the time-varying/constant coefficients, compared to the naive methods. An application to the dataset from the Atherosclerosis Risk in Communities Study, which is also the motivating example to develop the method, is presented.

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“…Several parametric methods have been proposed for the analysis of multistate models based on clustered observations ( Cook et al, 2004 ; Li and Zhang, 2015 ; Yiu et al, 2018 ). However, these methods impose strong parametric assumptions about the underlying multistate processes that are expected to be violated in practice.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric analysis of nonhomogeneous multistate processes with clustered observations

Bakoyannis

2020

Biometrics

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Frequently, clinical trials and observational studies involve complex event history data with multiple events. When the observations are independent, the analysis of such studies can be based on standard methods for multistate models. However, the independence assumption is often violated, such as in multicenter studies, which makes standard methods improper. This work addresses the issue of nonparametric estimation and two‐sample testing for the population‐averaged transition and state occupation probabilities under general multistate models with cluster‐correlated, right‐censored, and/or left‐truncated observations. The proposed methods do not impose assumptions regarding the within‐cluster dependence, allow for informative cluster size, and are applicable to both Markov and non‐Markov processes. Using empirical process theory, the estimators are shown to be uniformly consistent and to converge weakly to tight Gaussian processes. Closed‐form variance estimators are derived, rigorous methodology for the calculation of simultaneous confidence bands is proposed, and the asymptotic properties of the nonparametric tests are established. Furthermore, I provide theoretical arguments for the validity of the nonparametric cluster bootstrap, which can be readily implemented in practice regardless of how complex the underlying multistate model is. Simulation studies show that the performance of the proposed methods is good, and that methods that ignore the within‐cluster dependence can lead to invalid inferences. Finally, the methods are illustrated using data from a multicenter randomized controlled trial.

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Clustered Multistate Models with Observation Level Random Effects, Mover–Stayer Effects and Dynamic Covariates: Modelling Transition Intensities and Sojourn Times in a Study of Psoriatic Arthritis

Cited by 7 publications

References 24 publications

Not all is lost: Functional recovery in older adults following emergency general surgery

Not all is lost: Functional recovery in older adults following emergency general surgery

Multistate analysis of multitype recurrent event and failure time data with event feedbacks in biomarkers

Nonparametric analysis of nonhomogeneous multistate processes with clustered observations

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