TITLE:
Performance of Bayesian Propensity Score Adjustment for Estimating Causal Effects in Small Clinical Trials
AUTHORS:
Airi Takagi, Takuhiro Yamaguchi
KEYWORDS:
Bayesian Estimation, Causal Inference, Propensity Score, Quasi-Complete Separation, Prior Distribution
JOURNAL NAME:
Open Journal of Statistics,
Vol.13 No.1,
February
20,
2023
ABSTRACT: Propensity
score (PS) adjustment can control confounding effects and reduce bias when
estimating treatment effects in non-randomized trials or observational studies.
PS methods are becoming increasingly used to estimate causal effects, including
when the sample size is small compared to the number of confounders. With numerous confounders, quasi-complete separation
can easily occur in logistic regression used for estimating the PS, but this has not been addressed. We focused on a Bayesian PS method to
address the limitations of quasi-complete separation faced by small trials.
Bayesian methods are useful because they estimate the PS and causal effects
simultaneously while considering the uncertainty of the PS by modelling it as a
latent variable. In this study, we conducted simulations to evaluate the
performance of Bayesian simultaneous PS estimation by considering the
specification of prior distributions for model comparison. We propose a method
to improve predictive performance with discrete outcomes in small trials. We
found that the specification of prior distributions assigned to logistic
regression coefficients was more important in the second step than in the first
step, even when there was a quasi-complete separation in the first step.
Assigning Cauchy (0, 2.5) to coefficients improved the predictive performance
for estimating causal effects and improving the balancing properties of the
confounder.