Disability evolution in multiple sclerosis: how to deal with missing transition times in the Markov model?
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.
Petiot
V
V
Service de Biostatistique et d'Informatique Médicale, CHRU Dijon, Dijon, France.
Quantin
C
C
Le Teuff
G
G
Chavance
M
M
Binquet
C
C
Abrahamowicz
M
M
Moreau
T
T
D000328:Adult; D004185:Disability Evaluation; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D008959:Models, Neurological; D009103:Multiple Sclerosis / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology; D011379:Prognosis; D012720:Severity of Illness Index; D013997:Time Factors