Health Research and Policy

Abstract

DATE: November 29, 2012
TIME: 1:15 - 3:00 pm
LOCATION: Li Ka Shing Center for Learning (LKSC)
291 Campus Dr, Room LK 209
1:15 pm - 3:00 pm
TITLE: A General Statistical Approach for Personalized Medicine
SPEAKER: Lu Tian, Assistant Professor of Health Research and Policy, Stanford

The randomized clinical trial provides important information on the comparison between a treatment of interest and its alternative in a target population. However, the "superiority" of a treatment established by a randomized clinical trial is only on the average sense, which implies that the superior treatment identified by clinical trial is not necessarily the optimal choice for every individual patient or every subgroup of patients. Ideally, a guideline can be provided to help clinicians to prescribe the treatment to patients who may be benefited from it and avoid to give it to those who may be harmed by the treatment. This requires constructing a scoring system based on personal characteristics and biomarker profile to stratify the population into different subgroups with group-specific treatment effects. In this paper, we reviewed several current approaches for constructing and evaluating such scoring systems. We will also propose a new class of methods for stratifying the patient population according to the personalized treatment effect. The validity of the method does not rely on specific parametric assumption and can be adapted to deal with high-dimensional features. Extensive numerical studies will be presented to support the method.

Suggested readings:
Zhao, L., Tian, L., Cai, T., Claggett, B. and Wei, L.J. Effectively Selecting a Target Population for Future Comparative Study, 2011. Harvard University Biostatistics Working Paper Series.

Zhao, Y., Zeng, D., Rush, A.J. and Kosorok, M.R. Estimating Individualized Treatment Rules Using Outcome Weighted Learning, 2012. JASA, Vol. 107, No. 499, p.1106-1118.

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