599 research outputs found
SOME CONTRIBUTIONS TO DATA DRIVEN INDIVIDUALIZED DECISION MAKING PROBLEMS
Recent exploration of the optimal individualized decision rule (IDR) for patients in precision medicine has attracted a lot of attentions due to the potential heterogeneous response of patients to different treatments. In the current literature, an optimal IDR is a decision function based on patients’ characteristics for the treatment that maximizes the expected outcome. My dissertation research mainly focuses on how to estimate optimal IDRs under various criteria given experimental data. In the first part of this dissertation, focusing on maximizing expected outcome, we propose an angle-based direct learning (AD-learning) method to efficiently estimate optimal IDRs with multiple treatments for various types of outcomes. This contributes to the literature, where many existing methods are designed for binary treatment settings with the interest of a continuous outcome. In the second part, motivated by complex individualized decision making procedures, we propose two new robust criteria to estimate optimal IDRs: one is to control the average lower tail of the subjects’ outcomes and the other is to control the individualized lower tail of each subject’s outcome. In addition to optimizing the individualized expected outcome, our proposed criteria take risks into consideration, and thus the resulting IDRs can prevent adverse events caused by the heavy lower tail of the outcome distribution. In the third part of this dissertation, motivated by the concept of Optimized Certainty Equivalent (OCE), we generalize the second part and propose a decision-rule based optimized covariates dependent equivalent (CDE) for individualized decision making problems. Our proposed IDR-CDE not only broadens the existing expected outcome framework in precision medicine but also enriches the previous concept of the OCE in the risk management. We study the related mathematical problem of estimating an optimal IDRs both theoretically and numerically.Doctor of Philosoph
Causal Inference under Data Restrictions
This dissertation focuses on modern causal inference under uncertainty and
data restrictions, with applications to neoadjuvant clinical trials,
distributed data networks, and robust individualized decision making.
In the first project, we propose a method under the principal stratification
framework to identify and estimate the average treatment effects on a binary
outcome, conditional on the counterfactual status of a post-treatment
intermediate response. Under mild assumptions, the treatment effect of interest
can be identified. We extend the approach to address censored outcome data. The
proposed method is applied to a neoadjuvant clinical trial and its performance
is evaluated via simulation studies.
In the second project, we propose a tree-based model averaging approach to
improve the estimation accuracy of conditional average treatment effects at a
target site by leveraging models derived from other potentially heterogeneous
sites, without them sharing subject-level data. The performance of this
approach is demonstrated by a study of the causal effects of oxygen therapy on
hospital survival rates and backed up by comprehensive simulations.
In the third project, we propose a robust individualized decision learning
framework with sensitive variables to improve the worst-case outcomes of
individuals caused by sensitive variables that are unavailable at the time of
decision. Unlike most existing work that uses mean-optimal objectives, we
propose a robust learning framework by finding a newly defined quantile- or
infimum-optimal decision rule. From a causal perspective, we also generalize
the classic notion of (average) fairness to conditional fairness for individual
subjects. The reliable performance of the proposed method is demonstrated
through synthetic experiments and three real-data applications.Comment: PhD dissertation, University of Pittsburgh. The contents are mostly
based on arXiv:2211.06569, arXiv:2103.06261 and arXiv:2103.04175 with
extended discussion
A reluctant additive model framework for interpretable nonlinear individualized treatment rules
Individualized treatment rules (ITRs) for treatment recommendation is an
important topic for precision medicine as not all beneficial treatments work
well for all individuals. Interpretability is a desirable property of ITRs, as
it helps practitioners make sense of treatment decisions, yet there is a need
for ITRs to be flexible to effectively model complex biomedical data for
treatment decision making. Many ITR approaches either focus on linear ITRs,
which may perform poorly when true optimal ITRs are nonlinear, or black-box
nonlinear ITRs, which may be hard to interpret and can be overly complex. This
dilemma indicates a tension between interpretability and accuracy of treatment
decisions. Here we propose an additive model-based nonlinear ITR learning
method that balances interpretability and flexibility of the ITR. Our approach
aims to strike this balance by allowing both linear and nonlinear terms of the
covariates in the final ITR. Our approach is parsimonious in that the nonlinear
term is included in the final ITR only when it substantially improves the ITR
performance. To prevent overfitting, we combine cross-fitting and a specialized
information criterion for model selection. Through extensive simulations, we
show that our methods are data-adaptive to the degree of nonlinearity and can
favorably balance ITR interpretability and flexibility. We further demonstrate
the robust performance of our methods with an application to a cancer drug
sensitive study
Causal Effect Random Forest Of Interaction Trees For Learning Individualized Treatment Regimes In Observational Studies: With Applications To Education Study Data
Learning individualized treatment regimes (ITR) using observational data holds great interest in various fields, as treatment recommendations based on individual characteristics may improve individual treatment benefits with a reduced cost. It has long been observed that different individuals may respond to a certain treatment with significant heterogeneity. ITR can be defined as a mapping between individual characteristics to a treatment assignment. The optimal ITR is the treatment assignment that maximizes expected individual treatment effects. Rooted from personalized medicine, many studies and applications of ITR are in medical fields and clinical practice. Heterogeneous responses are also well documented in educational interventions. However, unlike the efficacy study in medical studies, educational interventions are often not randomized. Study results often suffer greatly from self-selection bias. Besides the intervention itself, the efficacy and effectiveness of interventions usually interact with a wide range of confounders.
In this study, we propose a novel algorithm to extend random forest of interaction trees to Casual Effect Random Forest of Interaction Trees (CERFIT) for learning individualized treatment effects and regimes. We first consider the study under a binary treatment setting. Each interaction tree recursively partitions the data into two subgroups with greatest heterogeneity of treatment effect. By integrating propensity score into the tree growing process, subgroups from the proposed CERFIT not only have maximized treatment effect differences, but also similar baseline covariates. Thus it allows for the estimation of the individualized treatment effects using observational data. In addition, we also propose to use residuals from linear models instead of the original responses in the algorithm. By doing so, the numerical stability of the algorithm is greatly improved, which leads to an improved prediction accuracy. We then consider the learning problem under non-binary treatment settings. For multiple treatments, through recursively partitioning data into two subgroups with greatest treatment effects heterogeneity with respect to two randomly selected treatment groups, the algorithm transforms the multiple learning ITR into a binary task. Similarly, continuous treatment can be handled through recursively partitioning the data into subgroups with greatest homogeneity in terms of the association between the response and the treatment within a child node. For all treatment settings, the CERFIT provides variable importance ranking in terms of treatment effects. Extensive simulation studies for assessing estimation accuracy and variable importance ranking are presented. CERFIT demonstrates competitive performance among all competing methods in simulation studies. The methods are also illustrated through an assessment of a voluntary education intervention for binary treatment setting and learning optimal ITR among multiple interventions for non-binary treatments using data from a large public university
- …