Tr. Tenhave et al., MIXED EFFECTS LOGISTIC-REGRESSION MODELS FOR LONGITUDINAL BINARY RESPONSE DATA WITH INFORMATIVE DROP-OUT, Biometrics, 54(1), 1998, pp. 367-383
A shared parameter model with logistic link is presented for longitudi
nal binary response data to accommodate informative drop-out. The mode
l consists of observed longitudinal and missing response components th
at share random effects parameters. To our knowledge, this is the firs
t presentation of such a model for longitudinal binary response data.
Comparisons are made to an approximate conditional legit model in term
s of a clinical trial dataset and simulations. The naive mixed effects
legit model that does not account for informative drop-out is also co
mpared. The simulation-based differences among the models with respect
to coverage of confidence intervals, bias, and mean squared error (MS
E) depend on at least two factors: whether an effect is a between- or
within-subject effect and the amount of between-subject variation as e
xhibited by variance components of the random effects distributions. W
hen the shared parameter model holds, the approximate conditional mode
l provides confidence intervals with good coverage for within-cluster
factors but not for between-cluster factors. The converse is true for
the naive model. Under a different drop-out mechanism, when the probab
ility of drop-out is dependent only on the current unobserved observat
ion, all three models behave similarly by providing between-subject co
nfidence intervals with good coverage and comparable MSE and bias but
poor within-subject confidence intervals, MSE, and bias. The naive mod
el does more poorly with respect to the within-subject effects than do
the shared parameter and approximate conditional models. The data ana
lysis, which entails a comparison of two pain relievers and a placebo
with respect to pain relief, conforms to the simulation results based
on the shared parameter model but not on the simulation based on the o
utcome-driven drop-out process. This comparison between the data analy
sis and simulation results may provide evidence that the shared parame
ter model holds for the pain data.