sensitivity analyses would be conducted by repeating
each analysis using inverse probability of attrition
weighting (IPAW). This propensity scoring approach
accounts for attrition-related selection bias in longitu-
dinal studies by more heavily weighting observations
associated with a lower probability of study completion
[40]. Thus, the resulting analysis is compensated for
under-representation of subjects who are more likely to
be lost to follow-up. IPAW produces smaller effect esti-
mate biases than more conventional methods that adjust
for baseline predictors of attrition [ 41]. Briefly, logistic
regression will be performed to identify predictors of
attrition using an opportunistic approach that optimizes
model fit, with an area under the curve (AUC) target
value of > 0.7. Demographic and baseline health variables
that differ between follow-up status cohorts will be used
as candidate variables for the regression model to derive
weights. Then, inverse of predicted probabilities for
remaining in the study will be used to weight observa-
tions, and all analyses will be repeated. Regression re-
sults using IPAW will be compared with those obtained
from complete case only analyses to assess the potential
influence of missing data on the findings and identify ro-
bust predictors. We will focus our interpretation on pre-
dictors that are consistent across complete case and
IPAW models for each type of healthcare service as they
are more robust and most likely to be reproduced in fu-
ture studies.
Power analysis
For logistic regression analyses, event-per-variable values
of 10 or greater are suggested, since overfitting will
weaken the probability that original findings will be
Lentz et al. BMC Health Services Research  (2018) 18:648 Page 5 of 14