MKVPCI: a computer program for Markov models with piecewise constant intensities and covariates

Alioum, Ahmadou; Commenges, Daniel

doi:10.1016/s0169-2607(00)00094-8

Cited by 33 publications

(56 citation statements)

References 12 publications

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“…A 5-state Markov model with piecewise constant transition intensities [26] was used to estimate the transition intensities between the 4 disability states and transition to death, and to study the effect of covariates on these transition intensities ( fig. 2).…”

Section: Discussionmentioning

confidence: 99%

The Contribution of Dementia to the Disablement Process and Modifying Factors

Barberger‐Gateau

Alioum

Pérès

et al. 2004

Dement Geriatr Cogn Disord

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The objective of this study was to examine the impact of dementia on disability progression and mortality, and to analyze the modifying effect of risk factors and extra-individual factors. A sample of 3,403 participants in the PAQUID study was followed for 10 years. Disability was assessed on a 4-grade scale: no disability, disabled only on the Rosow-Breslau scale, disabled on the Rosow-Breslau scale and on instrumental activities of daily living (IADL) scales, and disabled on the Rosow-Breslau, IADL and activities of daily living (ADL) scales. A Markov model was used to estimate the effect of explanatory variables on disability and mortality. Controlling for age, gender, education, place of residence, medical care and informal support, dementia had a strong significant effect on progression to IADL and then to ADL disability. Dementia did not increase the risk of death, once disability was taken into account, except from the lowest disability grade.

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Section: Discussionmentioning

confidence: 99%

The Contribution of Dementia to the Disablement Process and Modifying Factors

Barberger‐Gateau

Alioum

Pérès

et al. 2004

Dement Geriatr Cogn Disord

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“…They are listed in the remainder of this section. The MKVPCI package, developed by Alioum and Commenges (2001), has much in common with the msm package. It aims at estimating a multi-state continuous-time Markov model with piecewise constant transition rates from panel data when the number of intervals does not exceed three.…”

Section: Statistical Inferencementioning

confidence: 99%

Software for multistate analysis

Willekens¹,

Putter²

2014

DemRes

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“…All baseline intensities and all regression coefficients were simultaneously estimated using the maximum likelihood criterion [28] . Figure 1 shows the three consecutive states considered in our Markov model [21] analyses and identifies the two transition intensities, used as the outcome variables in the multistate analysis.…”

Section: Markov Multistate Modeling Of Ms Disability Progressionmentioning

confidence: 99%

“…MKVPCI v1.0 program [21] was used to estimate the Markov model parameters. Parametric regression model for the multiple imputations was implemented using the Lifereg procedure of SAS software v8.2.…”

Section: Comparing the Performance Of Multiple Imputation And Completmentioning

confidence: 99%

“…These states were defined according to the different forms of the disease: pure relapse (without sequelae), relapse with sequelae, progression, death and loss to follow-up. We propose here to use a similar approach, based on a Markov model [21] , but where the states are defined by the consecutive EGS scores, to identify the prognostic factors for disability progression. However, this approach requires information on the time of transition from one state to another and, sometimes, this information may be missing due to measurement problems.…”

Section: Introductionmentioning

confidence: 99%

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Disability Evolution in Multiple Sclerosis: How to Deal with Missing Transition Times in the Markov Model?

et al. 2007

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Markov modeling of disability progression in multiple sclerosis requires knowledge of all times of transitions from a given level of disability to the next level, but such data are often missing. We address methodological challenges due to partly missing transition times. To estimate the effects of prognostic factors on the risk of transitions between three consecutive disability levels, two methods were used to deal with missing data. Listwise deletion limited the analysis to subjects with complete data. Multiple imputation of missing data revealed that data were missing at random (MAR mechanism) and imputed the missing transition times from the Weibull model. The results were then compared with the full data set with the actual times established through chart review. Multiple imputation estimates were systematically closer to those from the full data set than the listwise deletion estimates.

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MKVPCI: a computer program for Markov models with piecewise constant intensities and covariates

Cited by 33 publications

References 12 publications

The Contribution of Dementia to the Disablement Process and Modifying Factors

The Contribution of Dementia to the Disablement Process and Modifying Factors

Software for multistate analysis

Disability Evolution in Multiple Sclerosis: How to Deal with Missing Transition Times in the Markov Model?

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