Statistics Seminar - Spring 2018

Seminar Schedule

Seminars are held on Thursdays at 4:00PM. Please note the location for each event in the schedule below, which will either be Cullimore 611 (CULM 611) or the Campus Center. For questions about the seminar schedule, please contact Antai Wang.


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Date Location Speaker, Affiliation, and Title Host
March 1 Campus Center,
Rm. 230
Prof. Thomas Mattew, University of Maryland at Baltimore County
Statistical Methods for Cost-Effectiveness Analysis: A Selected Review

Identifying treatments or interventions that are cost-effective (more effective at lower cost) is clearly important in health policy decision making, especially in the allocation of health care resources. Various measures of cost-effectiveness that are informative, intuitive and simple to explain have been suggested in the literature, along with statistical inference concerning them. Popular and widely used measures include the incremental cost-effectiveness ratio (ICER), defined as the ratio between the difference of expected costs and the difference of expected effectiveness in two populations receiving two treatments. Although very easy to interpret as the additional cost per unit of effectiveness gained, being a ratio, the ICER presents difficulties regarding interpretation in certain situations, for example, when the difference in effectiveness is close to zero, and it also presents challenges in the statistical inference. Yet another measure proposed in the literature is the incremental net benefit (INB), which is the difference between the incremental cost and the incremental effectiveness after multiplying the latter with a “willingness-to-pay parameter”. Both ICER and INB are functions of population means, and inference concerning them has been widely investigated under a bivariate normal distribution, or under a log-normal/normal distribution for the cost and effectiveness measures. In the talk, we will briefly review these, focusing on recent developments. An alternative probability-based approach will also be introduced, referred to as cost-effectiveness probability (CEP), which is the probability that the first treatment will be less costly and more effective compared to the second one. Inference on the CEP will also be discussed. Numerical results and illustrative examples will be given.
Antai Wang
March 22 CULM 611 Prof. Jiangtao Gou from Fox Chase Cancer Center, Temple University Health System
Multiple Endpoints in Clinical Trials: P-value Based Tests, Dependence Assumptions, and Group Sequential Procedures

The design and analysis of clinical trials often involve multiple endpoints. It is desirable to correctly and effectively adjust multiplicity in order to ensure valid statistical inference in confirmatory studies. In this presentation, we first introduce a family of p-value based procedures which have a step-up structure similar to the Hochberg procedure and uniformly improves upon the Hochberg procedure. We further discuss the dependence assumptions for controlling the error rate for correlated endpoints. In addition, we study the problem of testing a primary and a secondary endpoint, subject to gatekeeping constraint, using a group sequential design.
Antai Wang
March 29 CULM LH3 Prof. Yichao Wu, University of Illinois at Chicago
Nonparametric Estimation of Multivariate Mixtures

A multivariate mixture model is determined by three elements: the number of components, the mixing proportions and the component distributions. Assuming that we are given the number of components and that each mixture component has independent marginal distributions, we propose a non-parametric method to estimate the component distributions in a multivariate mixture model. The basic idea is that we convert the estimation of density functions as a problem of estimating the coordinates of density functions under a good set of basis functions. Specifically, we construct a set of basis functions by using conditional density functions and try to recover the coordinates of component distributions under this basis. Furthermore, we show that our estimator for the component density functions are consistent. In the simulation study, we compare our algorithm with other existing non-parametric methods of estimating component distributions in mixture models under the assumption of conditionally independent marginal.
Antai Wang
April 5 Campus Center,
Rm. 230
Dr. Xiaodong Luo, Sanofi
Points of Consideration for Non-constant Hazard Ratios in Survival Analyses

In this talk, I will discuss some issues in design of survival trials accounting for complex scenarios such as delayed treatment effect, treatment dilution and treatment crossover. These scenarios often lead to non-proportional hazards, making study design and monitoring more difficult. I will compare two popular methods (log-rank vs. restricted mean survival time) through examples in these non-proportional scenarios.
Antai Wang
April 19 CULM 611 Prof. Yaqun Wang, Rutgers School of Public Health
(Seminar information to follow)
TBA
May 3 CULM 611 Prof. Xiaoli Gao, University of North Carolina at Greensboro
(Seminar information to follow)
TBA
May 7 CULM 611 Prof. Yuan Ao, Georgetown University
(Seminar information to follow)
TBA

Updated: April 4, 2018