  Though they may offer valuable patient and disease information that is
impossible to study in a randomized trial, clinical disease registries also
require special care and attention in causal inference. Registry data may be
incomplete, inconsistent, and subject to confounding. In this paper we aim to
address several analytical issues in estimating treatment effects that plague
clinical registries such as the Emory amyotrophic lateral sclerosis (ALS)
Clinic Registry. When attempting to assess the effect of a surgical insertion
of a percutaneous endoscopic gastrostomy (PEG) tube on body mass index (BMI)
using the data from the ALS Clinic Registry, one must combat issues of
confounding, censoring by death, and missing outcome data that have not been
addressed in previous studies of PEG. We propose a causal inference framework
for estimating the survivor average causal effect (SACE) of PEG, which
incorporates a model for generalized propensity scores to correct for
confounding by pre-treatment variables, a model for principal stratification to
account for censoring by death, and a model for the missing data mechanism.
Applying the proposed framework to the ALS Clinic Registry Data, our analysis
shows that PEG has a positive SACE on BMI at month 18 post-baseline; our
results likely offer more definitive answers regarding the effect of PEG than
previous studies of PEG.
