Session title: Advances in estimation and prediction for understanding complex disorders
Organizer: Heping Zhang (Yale)
Chair: Naveen Narisetty (UIUC)
Time: June 4th, 11:00am-12:30pm
Location: VEC 902
Speech 1: Uncertainty Quantification of Treatment Regime in Precision Medicine by Confidence Distributions
Speaker: Min-ge Xie (Rutgers)
Abstract: Personalized decision rule in precision medicine can be viewed as a “discrete parameter”, for which theoretical development for statistical inference is lacking. In this talk, we propose a new way to quantify the estimation uncertainty in a personalized decision based on recent developments of confidence distribution (CD). Specifically, in a parametric regression model setup, suppose the decision for treatment versus control for an individual xa is determined by a linear decision rule Da = I(xab> xag), where b and g are unknown regression coefficients in models for potential outcomes of treatment and control, respectively. The data-driven decision hat-Da relies on the estimates of b and g, which in turn introduces uncertainty on the decision. In this work, we propose to find a CD for ha =xab – xag and compute a “confidence measure” of the decision {Da = 1} = {ha > 0}. This measure has a value between 0 and 1, and provides a frequency-based assessment on how reliable our decision is. For example, if the confidence measure of the decision {Da = 1} is 63%, then we know that, out of 100 patients who are the same as patient xa, 63 will benefit to have the treatment and 38 will be better off to be in the control group. Numerical study suggests that this new measurement is inline with classical assessments (such as sensitivity, specificity, etc.), but different from the classical assessments, this measurement can be directly computed from the observed data without the need to know the truth of {Da = 1} or {Da = 0}. Utility of this new measure will also be demonstrated in an application of an adaptive-design clinical trial. (Joint work with Yilei Zhan and Sijian Wang)Personalized decision rule in precision medicine can be viewed as a “discrete parameter”, for which theoretical development for statistical inference is lacking. In this talk, we propose a new way to quantify the estimation uncertainty in a personalized decision based on recent developments of confidence distribution (CD). Specifically, in a parametric regression model setup, suppose the decision for treatment versus control for an individual xais determined by a linear decision rule Da = I(xab> xag), where b and g are unknown regression coefficients in models for potential outcomes of treatment and control, respectively. The data-driven decision hat-Da relies on the estimates of andg, which in turn introduces uncertainty on the decision. In this work, we propose to find a CD for ha =xab – xag and compute a “confidence measure” of the decision Da = 1} = {ha > 0}. This measure has a value between 0 and 1, and provides a frequency-based assessment on how reliable our decision is. For example, if the confidence measure of the decision Da = 1} is 63%, then we know that, out of 100 patients who are the same as patient xa, 63 will benefit to have the treatment and 38 will be better off to be in the control group. Numerical study suggests that this new measurement is inline with classical assessments (such as sensitivity, specificity, etc.), but different from the classical assessments, this measurement can be directly computed from the observed data without the need to know the truth of Da = 1} or Da = 0}. Utility of this new measure will also be demonstrated in an application of an adaptive-design clinical trial. (Joint work with Yilei Zhan and Sijian Wang)
Speech 2: Semiparametric Estimation in the Secondary Analysis of Case-Control Studies
Speaker: Yanyuan Ma (Penn State)
Abstract:
Speech 3: Quantile Decision Trees and Forest with its application for predicting the risk (Post-Traumatic Stress Disorder) PTSD after experienced an acute coronary syndrome
Speaker: Ying Wei (Columbia)
Abstract: Classification and regression trees (CART) are a classic statistical learning method that efficiently partitions the sample space into mutually exclusive subspaces with the distinctive means of an outcome of interest. It is a powerful tool for efficient subgroup analysis and allows for complex associations and interactions to achieve high prediction accuracy and stability. Hence, they are appealing tools for precision health applications that deal with large amounts of data from EMRs, genomics, and mobile data and aim to provide a transparent decision mechanism. Although there is a vast literature on decision trees and random forests, most algorithms identify subspaces with distinctive outcome means. The most vulnerable or high-risk groups for certain diseases are often patients with extremely high (or low) biomarker and phenotype values. However, means-based partitioning may not be effective for identifying patients with extreme phenotype values. We propose a new regression tree framework based on quantile regression \cite{KoenkerBassett1978} that partitions the sample space and predicts the outcome of interest based on conditional quantiles of the outcome variable. We implemented and evaluated the performance of the conditional quantile trees/forests to predict the risk of developing PTSD after experiencing an acute coronary syndrome (ACS), using an observational cohort data from the REactions to Acute Care and Hospitalization (REACH) study\cite{onge2017depressive} at New York Presbyterian Hospital. The results show that the conditional quantile based trees/forest have better discrimination power to identify patients with severe PTSD symptoms, in comparison to the classical mean based CART.