Modeling drop-outs in amyotrophic lateral sclerosis.
Author(s): Messina P, Beghi E.
Affiliation(s): Laboratory of Neurological Disorders, Mario Negri Institute for Pharmacological
Research, Via La Masa 19, 20156, Milan, Italy. paolo.messina@marionegri.it
Publication date & source: 2012, Contemp Clin Trials. , 33(1):218-22
Amyotrophic lateral sclerosis (ALS) clinical trials suffer a large proportion of
drop-outs. Ignoring missing data can lead not only to underpowered tests, but
also to selection bias. Current strategies for handling not at random missing
data have several limitations. To determine the most effective approach, we
compared the standard procedures with the pattern mixture model, using the data
from a randomized dose-finding trial on lithium for the treatment of ALS, which
reported a high rate of drop-outs (68.4%). We evaluated the ALS Functional Rating
Scale-Revised (ALSFRS-R) profile using mixed effect models on different reference
populations (1. Intention-to-treat, 2. "Completers", 3. Last observation carried
forward, 4. "0-imputation"). All four strategies have limitations on account of:
1. Violation of the "missing completely at random" assumption of the mixed model;
2. Underpowered results on selected patients; 3. Underestimation of the time
effect on ALSFRS-R decline and misuse of the assumption that those who
discontinued could not get worse; 4. Overestimation of the time effect on
ALSFRS-R decline and misuse of the assumption that those who discontinued could
not have scores different from zero. The pattern mixture models fitted better
than models that did not consider the missing data pattern effect (p=0.006 and
p=0.0002). Pattern mixture model thus seem superior and we recommend its use to
obtain more accurate estimates even when the information is missing.
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