Causal inference in longitudinal observational health data often requires the accurate estimation of treatment effects on time-to-event outcomes in the presence of time-varying covariates. To tackle this sequential treatment effect estimation problem, we have developed a causal dynamic survival model (CDSM) that uses the potential outcomes framework with the Bayesian recurrent sub-networks  to estimate the difference in survival curves  . Using simulated survival datasets, CDSM has shown good causal effect estimation performance across scenarios of sample dimension, event rate, confounding and overlapping. However, we found increasing the sample size is not effective if the original data is highly confounded or with low level of overlapping . In two large clinical cohort studies, our model identified the expected conditional average treatment effect and detected individual effect heterogeneity over time and patient subgroups. The model provides individualized absolute treatment effect estimations that could be used in recommendation systems .
Causal inference in longitudinal observational health data often requires the accurate estimation of treatment effects on time-to-event outcomes in the presence of time-varying covariates. To tackle this sequential treatment effect estimation problem, we have developed a causal dynamic survival (CDS) model that uses the potential outcomes framework with the recurrent sub-networks with random seed ensembles to estimate the difference in survival curves of its confidence interval . Using simulated survival datasets, the CDS model has shown good causal effect estimation performance across scenarios of sample dimension, event rate, confounding and overlapping. However, increasing the sample size is not effective to alleviate the adverse impact from high level of confounding . In two large clinical cohort studies, our model identified the expected conditional average treatment effect and detected individual effect heterogeneity over time and patient subgroups. CDS provides individualised absolute treatment effect estimations to improve clinical decisions .
R
model (CDSM)
(CDS) model
D
Bayesian
A
with random seed ensembles
A
of its confidence interval
R
CDSM
the CDS model
D
we found
R
if the original data is highly confounded or with low level of overlapping
to alleviate the adverse impact from high level of confounding
R
The model provides individualized
CDS provides individualised
R
that could be used in recommendation systems
to improve clinical decisions