2007
DOI: 10.1159/000098518
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Disability Evolution in Multiple Sclerosis: How to Deal with Missing Transition Times in the Markov Model?

Abstract: 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. Mult… Show more

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Cited by 6 publications
(5 citation statements)
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“…There are many ways to model age-related cognitive changes, including Markov chains with Cox regression [18,19] logistic regression [20] , polytomous logistic regression [21,22] with adjustments for cofactors. These models, however, operate with a relatively small number of states ( !…”
Section: Discussionmentioning
confidence: 99%
“…There are many ways to model age-related cognitive changes, including Markov chains with Cox regression [18,19] logistic regression [20] , polytomous logistic regression [21,22] with adjustments for cofactors. These models, however, operate with a relatively small number of states ( !…”
Section: Discussionmentioning
confidence: 99%
“…A Markov model is a general MSM in which a system switches between different states assuming a certain transition probability. Multistate Markov models are commonly used in studies of chronic diseases, in which patients are assumed to pass through a series of discrete disease stages with 1 final state, either death or the terminal stage of a disease from which no exacerbation is possible, that is irreversible and referred to as an “absorbing” state . This approach works very well for censored data and has been implemented in the software solution R (R Statistical Foundation; add-on package msm) …”
Section: Methodsmentioning
confidence: 99%
“…Multistate Markov models are commonly used in studies of chronic diseases, in which patients are assumed to pass through a series of discrete disease stages with 1 final state, either death or the terminal stage of a disease from which no exacerbation is possible, that is irreversible and referred to as an "absorbing" state. 12,13 This approach works very well for censored data 14 and has been implemented in the software solution R (R Statistical Foundation; add-on package msm). 15 With the addition of a dropout state, ie, treatment cessation for any reason including mortality, as an absorbing state, our data reflected a 7-state system: 6 vision states as well as 1 absorbing health state in which patients were no longer treated.…”
Section: Statistical Analysesmentioning
confidence: 99%
“…Progression is characterized by a 6-month period of continuous deterioration in neurological status, while relapse is defined as the occurrence or aggravation of neurological symptoms lasting for more than 24 h (23,24). These attacks should be separated by at least 30 days in order to be considered a relapse.…”
Section: Clinical Characteristics: Children Versus Adultsmentioning
confidence: 99%