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Song Zhang of the Department of Biostatistics defends her dissertation on "Diagnostic Accuracy Analysis for Ordinal Competing Risks Outcomes Using ROC Surface".
Yongli Shuai of the Department of Biostatistics defends his dissertation on "Multinomial Logistic Regression and Prediction Accuracy for Interval-Censored Competing Risks Data".
Judah Abberbock of the Department of Biostatistics defends his dissertation on "Usage of Surrogate Endpoints in the Design and Analysis of Clinical Trials"
Graduate faculty of the University and all other interested parties are invited to attend
There has been a shift in the conduct of early-stage breast cancer trials in recent years from long adjuvant trials with overall or disease-free survival as the efficacy endpoint to shorter neoadjuvant trials with pathological complete response (pCR), a binary marker, at time of surgery as the endpoint. The Food and Drug Administration (FDA) currently embraces this transition and deems evidence in pCR improvement sufficient for drug approval on condition that long-term data are collected to eventually show efficacy in survival. Incorporating data on pCR in the design and analysis of such a trial is therefore of public health interest. Here, we propose one method to assess the power and sample size of such a trial with using observed neoadjuvant data and another method to estimate certain causal treatment effects on survival conditional on pCR. In the first part, we propose an exponential mixture model for survival time with parameters for the response rates and an estimated benefit in survival from achieving response. Under a fixed sample size, we obtain the empirical power through simulations from the proposed mixture model. We also propose a more efficient method than the empirical approach by applying an estimated average hazard ratio to the Schoenfeld formula. The performance of our methods is assessed via simulation studies. Data from two neoadjuvant cancer clinical trials are used to illustrate these methods. Second, we propose a method under the principal stratification framework to estimate the causal effect of treatment on a binary outcome, conditional on a post-treatment binary response marker in randomized controlled clinical trials. Specifically, we estimate the treatment effect among those who would achieve response if given the treatment. We are able to identify this causal effect under two assumptions. First, we model the counterfactual probability of achieving response under treatment given baseline clinical markers and the outcome. Second, we assume a monotonicity condition: a patient who responds under control would respond under treatment as well. We compared the performance of proposed method with other standard approaches in simulation studies. Data from a neoadjuvant breast cancer clinical trial are used to demonstrate the proposed method.
Last Updated On Monday, October 23, 2017 by Valenti, Renee Nerozzi
Created On Tuesday, May 30, 2017
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