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Volume 24 (2024)
Volume 24, Issue 3 (In progress)
Pg 399 - 526 (November 2024)
Volume 24, Issue 2
Pg 197 - 397 (July 2024)
Volume 24, Issue 1
Pg 1 - 196 (March 2024)
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Pg 1 - 94 (March 2023)
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Pg 1 - 84 (December 2022)
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Pg 1 - 154 (September 2022)
Volume 20 (2022)
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Pg 1 - 123 (June 2022)
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Volume 19,
Pg 1 - 144 (March 2022)
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Volume 18, Issue 3
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Pg 149 - 303 (August 2021)
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Pg 213 - 311 (October 2010)
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Pg 107 - 212 (June 2010)
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JP Journal of Biostatistics
JP Journal of Biostatistics
Volume 4, Issue 3, Pages 289 - 311 (October 2010)
MODELLING EVOLUTION OF MULTIPLE SCLEROSIS: EVIDENCE FROM SURVIVAL AND LONGITUDINAL APPROACHES
Clelia Di Serio and Daniela Zugna
Abstract:
In both clinical trial as well as natural history studies, disease progression is monitored over time, by analyzing the evolution of a disease marker in relation to other variables and to some event of interest. However, when the disease process of interest is reversible, such as in multiple sclerosis (MS) studies, standard survival tools may fail in describing its evolution over time since informative censoring occurs. If the disease marker is represented by an ordinal variable with non-constant increments, as commonly are functional disability markers (EDSS, FSS and so on), also standard longitudinal models based on continuous response variables cannot be used. Within these frameworks, we propose a probit ordered model with random effects to account together for longitudinality, informative censoring and reversibility of the process, and to provide tools which are interpretable in predictive terms.
Keywords and phrases:
multiple sclerosis, ordered probit model, random effects, Cox model, informative censoring.
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P-ISSN: 0973-5143
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