Open AccessDisability Evolution in Multiple Sclerosis: How to Deal with Missing Transition Times in the Markov Model?
Author(s) -
V. Petiot,
Catherine Quantin,
Gwénaël Le Teuff,
Michel Chavance,
Christine Binquet,
Michał Abrahamowicz,
Thibault Moreau
Publication year - 2007
Publication title -
neuroepidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.217
H-Index - 87
eISSN - 1423-0208
pISSN - 0251-5350
DOI - 10.1159/000098518
Subject(s) - missing data , imputation (statistics) , medicine , markov chain , data set , multiple sclerosis , statistics , data mining , computer science , mathematics , psychiatry
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.