||1R21CA245855-01A1 Interpret this number
||Icahn School Of Medicine At Mount Sinai
||Flexible Bayesian Approaches to Causal Inference with Multilevel Survival Data and Multiple Treatments
Combining comparative effectiveness research (CER) and dissemination and implementation research is playing
an increased role in public health and health care service by allowing practitioners to make informed decisions
about treatments and improving adoption of evidence-based practices. In circumstances where CER questions
do not lend themselves to direct experimentation or in implementation trials where incomplete adoption of in-
tervention occurs, causal inference tools for “ﬁeld data” are recommended for evaluating treatment effects. The
increased complexities in large national electronic health databases pose challenges for statistical analyses and
demand approaches beyond conventional causal inference techniques, which have traditionally focused on bi-
nary treatment. Given the wealth of information captured in large-scale data, it is rare that treatment regimens
are deﬁned in terms of two treatments only. The data are typically pooled from treating facilities across the nation
with considerable variability in the institutional effect. Although it has been established that popular tools for bi-
nary treatment are inappropriate for the multiple treatment setting, and that ignoring the multilevel data structure
can bias the estimate of the treatment effect, few alternative methods have been proposed to deal with both
complications simultaneously. The ﬁrst aim of our proposed project is to develop a novel and ﬂexible Bayesian
approach to estimating the causal effects of multiple treatments on survival with clustered data. We then fully
investigate the operating characteristics of our proposed method in a variety of simulated scenarios and contrast
it with approaches often used in practice. For causal estimates to be unbiased, researchers commonly make the
assumption of no unmeasured confounding (UMC). Though highly recommended with binary treatment, there
is no known implementation or framework for sensitivity analysis with multiple treatments and multilevel survival
data. The second aim of our project is to develop and apply a ﬂexible and interpretable Bayesian approach to
assessing the sensitivity of causal estimates to possible departures from the assumption of no UMC, at both
cluster- and individual-level. This approach is capable of gauging the amount of unobserved confounding needed
to change the direction of the observed treatment effects Our project will apply the developed methods in the ﬁrst
two aims to a large representative high-risk localized prostate cancer population, drawn from the National Cancer
Data Base, to evaluate the average causal effects of three popular treatment options on survival and evaluate
how unmeasured confounding might alter causal conclusions. We also will estimate treatment heterogeneity and
identify distinct subgroups of patients for which a treatment is effective or harmful. Our methods will establish the
effectiveness component and lay the groundwork for building the cost-effectiveness models, and provide evidence
for further investigations of variations in intervention implementation and modiﬁcations in recommendations for
treatments leading to different patient outcomes. To facilitate the dissemination of our work, we will share the
underlying statistical code via an R package.
Identifying and understanding determinants of high healthcare costs for breast cancer: a quantile regression machine learning approach.
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BMC health services research, 2020-11-23; 20(1), p. 1066.
Identifying and assessing the impact of key neighborhood-level determinants on geographic variation in stroke: a machine learning and multilevel modeling approach.
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, Ji J.
, Li Y.
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, Zhang Y.
Journal of urban health : bulletin of the New York Academy of Medicine, 2020-09-04; , .