The Validity of Inferences Based on Incomplete Observations in Disease State Models

Grüger, J.; Kay, Richard; Schumacher, Martin

doi:10.2307/2532149

Cited by 112 publications

(98 citation statements)

References 25 publications

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“…Grüger et al [57] in the context of Markov models considered the effect of dependent sampling on the validity of inferences. This is an important area because the construction of the likelihood for panel observed data depends on the assumption that the observation process can be ignored.…”

Section: Grüger Et Al : the Effect Of Dependent Samplingmentioning

confidence: 99%

Model diagnostics for multi-state models

Titman

Sharples

2009

Stat Methods Med Res

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Multi-state models are a popular method of describing medical processes that can be represented as discrete states or stages. They have particular use when the data are panel-observed, meaning they consist of discrete snapshots of disease status at irregular time points which may be unique to each patient. However, due to the difficulty of inference in more complicated cases, strong assumptions such as the Markov property, patient homogeneity and time homogeneity are applied. It is important that the validity of these assumptions is tested. A review of methods for diagnosing model fit for panel-observed continuous-time Markov and misclassification-type hidden Markov models is given, with illustrative application to a dataset on cardiac allograft vasculopathy progression in post-heart transplant patients.

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Section: Grüger Et Al : the Effect Of Dependent Samplingmentioning

confidence: 99%

Model diagnostics for multi-state models

Titman

Sharples

2009

Stat Methods Med Res

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“…Similar to the definition of noninformative censoring for right-censored data, Gruger et al (10) defined noninformative examination schemes (or sampling plans) for interval censoring problems. Basically, an examination scheme (including the number of examinations and their times) is called noninformative if the likelihood function given the examination scheme is proportional to the likelihood function obtained when the examination scheme is fixed in advance.…”

Section: Interval Censoringmentioning

confidence: 99%

“…Under the above examination schemes, Gruger et al (10) showed that the likelihood function (Eq. 4) can be used to obtain an estimate of the survival function (4,23) or estimates of the regression coefficients of survival times on the covariates (7,18,24).…”

Section: Doctor's Carementioning

confidence: 99%

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Censoring Issues in Survival Analysis

Leung

Elashoff

Afifi

1997

Annu. Rev. Public Health

277

181

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A key characteristic that distinguishes survival analysis from other areas in statistics is that survival data are usually censored. Censoring occurs when incomplete information is available about the survival time of some individuals. We define censoring through some practical examples extracted from the literature in various fields of public health. With few exceptions, the censoring mechanisms in most observational studies are unknown and hence it is necessary to make assumptions about censoring when the common statistical methods are used to analyze censored data. In addition, we present situations in which censoring mechanisms can be ignored. The effects of the censoring assumptions are demonstrated through actual studies.

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“…and if the inspection process is non-informative, we disregard the second term on the right (Grüger et al, 1991). The factorization…”

Section: Likelihood With Intermittent Observationmentioning

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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The Validity of Inferences Based on Incomplete Observations in Disease State Models

Cited by 112 publications

References 25 publications

Model diagnostics for multi-state models

Model diagnostics for multi-state models

Censoring Issues in Survival Analysis

Statistical Issues in Modeling Chronic Disease in Cohort Studies

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