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  吉林大学应用统计研究中心成立于2011年,旨在推动应用统计方法的发展,并应用统计手段来描述并帮助解决各种实际生活中的问题。这些问题包括在经济学、流行病学、医学科学和社会实验等研究中所产生的问题。中心成立的具体目的是:

  1. 对应用统计学人才进行教育和培训……

学术会议
Jilin Univ First Biostatistics Workshop
发表于: 2012-04-20 10:01  点击:864

吉林大学第一届国际生物统计专题研讨会

 

  时间: 2011713(星期三)

  地点: 吉林大学数学学院一楼报告厅

组委会: 王德辉 孙建国 赵世舜

 

会议日程

 

报告时间

主持人

报告人

报告题目

9:00-9:20

孙建国

开幕式

 

9:20-10:20

王德辉

Dr. Dianliang Deng

Modeling Sustained Treatment Effects in Tumor Xenograft Experiments.

10:20-10:40

 

Photo and break

 

10:40-11:40

王德辉

Dr. Hongbin Fang

Efficient Design and Analysis for Drug Combination Studies to Improve Early Stage Clinical Development

11:40-13:30

 

Lunch

 

13:30-14:30

孙建国

Dr. Gang Li

Latent Subgroup Survival Analysis with Applications to Oncology Trials

14:30-14:45

 

Break

 

14:45-15:45

孙建国

Dr. Peter Song

Merging Surveillance Cohorts in HIV/AIDS Studies -- A Case Study

15:45-16:00

 

Break

 

16:00-17:00

孙建国

Dr. Guosheng Yin

Bayesian Adaptive Dose-Finding Methods for Drug Combinations

17:20-20:30

 

Dinner

 

 

 

会议报告摘要

Modeling Sustained Treatment Effects in Tumor Xenograft Experiments.

Dianliang Deng, PhD

Department of Statistics

University of Regina

 

 In cancer drug development, demonstrated efficacy in tumor xenograft models is an important step to bring a promising compound to human. A key outcome variable is tumor volume measured over a period of time, while mice are treated with certain treatment regimens. The statistical challenges include that sample sizes in xenograft experiments are usually limited because these experiments are costly, tumors in mice without treatment would keep growing until the tumor-bearing mice die or are sacrificed, and missing data are unavoidable because a mouse may die of toxicity or may be sacrificed when its tumor volume reaches certain threshold (i.e. quadruples) or the tumor volume is too small and becomes unmeasurable. Furthermore, since the drug concentration in the blood of a mouse or its tissues may be stabilized at a certain level and maintained during a period of time, the treatment effect due to sustained drug release in tumor xenograft models should be taken into account. This paper proposes a novel comprehensive statistical model that accounts for the sustained release effects in tumor xenograft experiments and parameter constraints with incomplete longitudinal data. The ECM algorithm and Gibbs sampling for incomplete data are applied to estimating the Dose-response relationship in the proposed model. The model selection based on likelihood functions is given and a simulation study is conducted to investigate the performance of the proposed estimator. A real xenograft study on the antitumor agent temozolomide combined with irinotecan against the rhabdomyosarcoma is analyzed using the proposed methods.

 

Efficient Design and Analysis for Drug Combination Studies

to Improve Early Stage Clinical Development

 

Hong-Bin Fang, Ph.D

Division of Biostatistics

University of Maryland Greenebaum Cancer Center

 

Drug combinations are the hallmark of cancer therapy. Preclinical experiments on multi-drug combinations are important steps to bring the therapy to clinic. A statistical approach for evaluating the joint effect of the combination is necessary because even in vitro experiments often demonstrate significant variation in dose-effect. Such variation needs to be accounted for in the experimental design and analysis. Our research has developed a maximal power design and interaction index surface analysis methods for in vitro and in vivo (e.g., xenograft) combination studies so that the joint effect of a combination can be estimated efficiently and the most synergistic combination can be identified. We demonstrate that these statistical methods and software have resulted in the identification of highly synergistic dose combinations that could have been missed with classic methods.

 

Latent Subgroup Survival Analysis with Applications to Oncology Trials

 

Gang Li, Ph.D

UCLA Department of Biostatistics and Jonsson Comprehensive Cancer Center

 

Subgroup analysis arises in clinical trials research when we wish to estimate a treatment effect on a specific subgroup of the population distinguished by a biomarker at baseline. Many trial designs induce latent subgroups such that subgroup membership is observable in one arm of the trial and unidentified in the other. This occurs, for example, in oncology trials when a biopsy or dissection is performed only on subjects randomized to active treatment. We discuss a general framework to estimate a biological treatment effect on the latent subgroup of interest when the survival outcome is right-censored. Our framework builds on the application of instrumental variables methods to all or-none treatment noncompliance. We derive a computational method to estimate model parameters and provide guidance on its implementation in standard software packages. The research is illustrated through an analysis of a seminal melanoma trial that proposed a new standard of care for the disease and involved a biopsy that is available only on patients in the treatment arm.

 

Merging Surveillance Cohorts in HIV/AIDS Studies -- A Case Study

 

Peter Song, Ph.D.

Department of Biostatistics, University of Michigan

 

Roaring HIV/AIDS prevalence in China has become a serious public health concern in recent years. Data from established surveillance networks across the country have provide timely information for intervention, control and prevention. In this talk, I will focus on the study population of drug injection users in Sichuan province over years 2006-2009, and investigate both longitudinal and spatial profiles of HIV infection patterns in this region.  In particular, I will introduce a newly developed estimating equation approach to merging longitudinal cohort study datasets, which enabled us to not only effectively detect risk factors associated with worsening disease prevalence rates but also to estimate the effect sizes of the detected risk factors. Both simulation studies and real data analysis will be presented.

 

 

 

 

Bayesian Adaptive Dose-Finding Methods for Drug Combinations

 

Guosheng Yin, Ph.D

Department of Statistics and Actuarial Science, The University of Hong Kong

 

Phase I oncology trials aim to find the maximum tolerated dose (MTD) for an investigational drug.  Such early-phase trials involve limited resources and especially a small sample size.  Treating patients with a combination of agents is becoming commonplace in cancer clinical trials. In a typical drug-combination trial, the toxicity profile of each drug has already been thoroughly studied in the single-agent trials.  We introduce a wide range of statistical methods that are commonly used for designing phase I drug-combination clinical trials from the Bayesian perspectives.  To search for the MTD combination, we update the posterior estimates for the toxicity probabilities of the combined doses. By reordering the dose toxicities in the two-dimensional space, we assign each new cohort of patients to the most appropriate dose. Dose escalation or de-escalation is determined by comparing the prespecified target and the posterior estimates of the toxicity probabilities of combined doses.  We examine the operating characteristics of these Bayesian adaptive designs through extensive simulation studies and illustrate them with practical scenarios.

 

 

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