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BATE curve in assessment of clinical utility of predictive biomarkers

Science China Mathematics 2012年第8期55卷 1529-1552页

作者： ZHOU XiaoHua 1,2,3,& MA YunBei 4,5 1 Northwest HSR&D Center of Excellence,VA Puget Sound Health Care System,Seattle,WA 98198,usa 2 department of Biostatistics,university of Washington,Seattle,WA 98198,usa 3 Beijing International Center for Mathematical research,Peking university,Beijing 100871,China 4 department of operations research and financial engineering,princeton university,princeton,nj 08540,usa 5 School of Statisitcs,Southwest university of Finance and Economics,Chengdu 611130,China 1. Northwest HSR&D Center of Excellence VA Puget Sound Health Care System Seattle WA 98198 USA2. Department of Biostatistics University of Washington Seattle WA 98198 USA3. Beijing International Center for Mathematical Research Peking University Beijing 100871 China4. Department of Operations Research and Financial Engineering Princeton University Princeton NJ 08540 USA5. School of Statisitcs Southwest University of Finance and Economics Chengdu 611130 China

In this paper, for time-to-event data, we propose a new statistical framework for casual inference in evaluating clinical utility of predictive biomarkers and in selecting an optimal treatment for a particular patient. This new casual framework is based on a new concept, called Biomarker Adjusted Treatment Effect （BATE） curve. The BATE curve can be used for assessing clinical utility of a predictive biomarker, for designing a subsequent confirmation trial, and for guiding clinical practice. We then propose semi-p^rametric methods for estimating the BATE curves of biomarkers and establish asymptotic results of the proposed estimators for the BATE curves. We also conduct extensive simulation studies to evaluate finite-sample properties of the proposed estimation methods. Finally, we illustrate the application of the proposed method in a real-world data set.

关键词： predictive biomarker cutoff points interaction BATE curve time-to-event outcome

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Gaining effciency via weighted estimators for multivariate failure time data

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Science China Mathematics 2009年第6期52卷 1113-1128页

作者： FAN JianQing1,2,ZHOU Yong2,3,CAI JianWen4 & CHEN Min3 1 department of operations research and financial engineering,princeton university,princeton,nj08544,usa 2 department of Statistics,Shanghai university of Finance and Economics,Shanghai 200433,China 3 Institute of Applied Mathematics,Academy of Mathematics and Systems Science,Chinese Academy of Sci-ences,Beijing 100190,China 4 department of Biostatistics,university of North Carolina at Chapel Hill,Chapel Hill,NC 27599-7420,usa

Multivariate failure time data arise frequently in survival analysis.A commonly used tech-nique is the working independence estimator for marginal hazard *** natural questions are how to improve the effciency of the working independence estimator and how to identify the situations under which such an estimator has high statistical *** this paper,three weighted estimators are proposed based on three different optimal criteria in terms of the asymptotic covariance of weighted ***-form solutions are found,which always outperform the working indepen-dence *** also prove that the working independence estimator has high statistical effciency,when asymptotic covariance of derivatives of partial log-likelihood functions is nearly exchangeable or *** are conducted to compare the performance of the weighted estimator and work-ing independence estimator.A data set from Busselton population health surveys is analyzed using the proposed estimators.

关键词： marginal hazard model pseudo-partial likelihood working independence estimator optimal weight

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Optimizing the Ex-post Trading Profits in a financial Market

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运筹学学报 1997年第1期1卷 2-13页

作者： KIN LAM（department of Finance and Decision Sciences, School of Business, Hong Kong Baptist university, Hong Kong) XIANG-SUN ZHANG(Institute of Applied Mathematics, The Chinese Academy of Sciences, Beijing, China) WEI LI（department of Finance and Decisi 香港浸会大学香港中科院应用数学所北京

Consider investing in an asset in a financial market. Given the investment returnri in day i, consider the problem of making an investmeat decision di in day i wheredi= 1,0, -1, has the meaning that the investor maintains a long, neutral or shortposition of the asset. The overall invest return over an n day period is given bywhere c=transaction cO8t and d0 = dn+1 = 0 are given. The optinization problemof maximizing IR(d) is considered. Analytic solution of the opthaation porblem isobtained. The solution is shown to be closely related to Alexander’s tilter trading ruleand has praCtical implication in applying neural network to generate trading signals ina financial maket.

关键词： filter trading rule optimization finance investment neural network training set

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