Model diagnostics for multi-state models

Titman, Andrew; Sharples, Linda D.

doi:10.1177/0962280209105541

Cited by 54 publications

(51 citation statements)

References 115 publications

(220 reference statements)

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“…For any model, be it parametric or non-parametric, this kind of extrapolation is not without danger and model validation is of specific interest. However, model validation is still a subject of research (Titman and Sharples 2010) and has not yet caught up with the current flexibility to create extended models. Nevertheless, some heuristic methods to assess model fit where presented in this paper.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification

Hout¹,

Ogurtsova²,

Gampe³

et al. 2014

DemRes

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Section: Discussionmentioning

confidence: 99%

“…Aguirre-Hernandez and Farewell (2002) and Titman and Sharples (2010) provide tests for a set of multi-state models, but this set does not include our model. However, Titman and Sharples also review less formal ways of model validation, and we use their basic ideas in the following validation of Model A.…”

Section: Model Validationmentioning

confidence: 99%

Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification

Hout¹,

Ogurtsova²,

Gampe³

et al. 2014

DemRes

View full text Add to dashboard Cite

“…In settings where processes are observed continuously, residual plots and other techniques for model assessment in survival analysis can be used, since sojourns in a state are equivalent to survival times in a competing risk model. When Markov models are adopted for processes under intermittent observation, it is possible to compare expected (model-based) transition counts and corresponding observed counts in some cases (Titman and Sharples, 2010b), and some such checks are provided by the msm package (Jackson, 2011). Further exploration of model checking methods would be valuable.…”

Section: Discussionmentioning

confidence: 99%

Statistical Issues in Modeling Chronic Disease in Cohort Studies

Cook

Lawless

2013

Stat Biosci

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SummaryObservational cohort studies of individuals with chronic disease provide information on rates of disease progression, the effect of fixed and time-varying risk factors, and the extent of heterogeneity in the course of disease. Analysis of this information is often facilitated by the use of multistate models with intensity functions governing transition between disease states. We discuss modeling and analysis issues for such models when individuals are observed intermittently. Frameworks for dealing with heterogeneity and measurement error are discussed including random effect models, finite mixture models, and hidden Markov models. Cohorts are often defined by convenience and ways of addressing outcome-dependent sampling or observation of individuals are also discussed. Data on progression of joint damage in psoriatic arthritis and retinopathy in diabetes are analysed to illustrate these issues and related methodology.

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“…Among other widely used methods to assess this assumption the most common is piecewise constant model (Faddy, 1976;Saint-Pierre et al, 2003;Titman, 2008;Titman and Sharples, 2010a). In this method, the transition rates among states are modeled as piecewise constant.…”

Section: In This Equation H(t)mentioning

confidence: 99%

Assessing Markov and Time Homogeneity Assumptions in Multi-state Models: Application in Patients with Gastric Cancer Undergoing Surgery in the Iran Cancer Institute

Zare

Mahmoodi

Mohammad³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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Background: Multi-state models are appropriate for cancer studies such as gastrectomy which have high mortality statistics. These models can be used to better describe the natural disease process. But reaching that goal requires making assumptions like Markov and homogeneity with time. The present study aims to investigate these hypotheses. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. To assess Markov assumption and time homogeneity in modeling transition rates among states of multi-state model, Cox-Snell residuals, Akaikie information criteria and Schoenfeld residuals were used, respectively. Results: The assessment of Markov assumption based on Cox-Snell residuals and Akaikie information criterion showed that Markov assumption was not held just for transition rate of relapse (state 1 state 2) and for other transition rates -death hazard without relapse (state 1 state 3) and death hazard with relapse (state 2 state 3) -this assumption could also be made. Moreover, the assessment of time homogeneity assumption based on Schoenfeld residuals revealed that this assumption -regarding the general test and each of the variables in the model -was held just for relapse (state 1 state 2) and death hazard with a relapse (state 2 state 3). Conclusions: Most researchers take account of assumptions such as Markov and time homogeneity in modeling transition rates. These assumptions can make the multi-state model simpler but

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Model diagnostics for multi-state models

Cited by 54 publications

References 115 publications

Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification

Investigating healthy life expectancy using a multi-state model in the presence of missing data and misclassification

Statistical Issues in Modeling Chronic Disease in Cohort Studies

Assessing Markov and Time Homogeneity Assumptions in Multi-state Models: Application in Patients with Gastric Cancer Undergoing Surgery in the Iran Cancer Institute

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