Precision medicine

Friday, November 9, 2018 - 10:40am - 11:10am
Min Zhang (University of Michigan)
A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual’s own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes.
Thursday, November 8, 2018 - 3:00pm - 3:30pm
Lei Liu (Washington University School of Medicine)
Mediation analysis has been commonly used to study the effect of an exposure on an outcome through a mediator. In practice, we may face the problem to estimate and test a specific mediator of interest, termed “targeted mediator, in the presence of high dimensional mediators. In this talk we present a de-biased Lasso estimate for the targeted mediator and derive its standard error estimator, which can be used to develop a test procedure for the targeted mediation effect. Extensive simulation studies are conducted to assess the performance of our method.
Thursday, November 8, 2018 - 2:30pm - 3:00pm
Feifang Hu (George Washington University)
Covariate-adjusted randomization procedure is frequently used in comparative studies (such as clinical trials for precision medicine) to increase the covariate balance across treatment groups. However, as the randomization inevitably uses the covariate information when forming balanced treatment groups, the validity of classical statistical methods following such randomization is often unclear.In this talk, we discuss the theoretical properties of statistical methods based on general covariate-adjusted randomization under the linear model framework.
Saturday, September 16, 2017 - 11:20am - 11:50am
Yi-Hsiang Hsu (Harvard Medical School)
The fast moving of the cancer immunotherapy field has generated tremendous excitement regarding new therapeutic strategies and will likely change the paradigm of therapeutic interventions for cancer. Neoantigens, generated by tumor-specific DNA alterations that result in the formation of novel protein sequences and only in cancer cells, represent an optimal target for the immune system and make possible a new class of highly personalized vaccines with the potential for significant efficacy with reduced side effects.
Thursday, September 14, 2017 - 2:00pm - 2:45pm
Eric Laber (North Carolina State University)
A treatment regime formalizes personalized medicine as a function from individual patient characteristics to a recommended treatment. A high-quality treatment regime can improve patient outcomes while reducing cost, resource consumption, and treatment burden. Thus, there is tremendous interest in estimating treatment regimes from observational and randomized studies. However, the development of treatment regimes for application in clinical practice requires the long-term, joint effort of statisticians and clinical scientists.
Thursday, September 14, 2017 - 4:30pm - 5:00pm
Lan Wang (University of Minnesota, Twin Cities)
Finding the optimal treatment regime (or a series of sequential treatment regimes) based on individual characteristics has important applications in areas such as precision medicine, government policies and active labor market interventions. In the current literature, the optimal treatment regime is usually defined as the one that maximizes the average benefit in the potential population. This paper studies a general framework for estimating the quantile-optimal treatment regime, which is of importance in many real-world applications.
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