Events

Monday, October 30, 2017 — 4:00 PM EDT

Analysis of Clinical Trials with Multiple Outcomes

In order to obtain better overall knowledge of a treatment effect, investigators in clinical trials often collect many medically related outcomes, which are commonly called as endpoints. It is fundamental to understand the objectives of a particular analysis before applying any adjustment for multiplicity. For example, multiplicity does not always lead to error rate inflation, or multiplicity may be introduced for purpose other than making an efficacy or safety claim such as in sensitivity assessments. Sometimes, the multiple endpoints in clinical trials can be hierarchically ordered and logically related. In this talk, we will discuss the methods to analyze multiple outcomes in clinical trials with different objectives:  all or none approach, global approach, composite endpoint, at-least-one approach.

Tuesday, October 31, 2017 — 1:00 PM EDT

Data Adaptive Support Vector Machine with Application to Prostate Cancer Imaging Data

Support vector machines (SVM) have been widely used as classifiers in various settings including pattern recognition, texture mining and image retrieval. However, such methods are faced with newly emerging challenges such as imbalanced observations and noise data. In this talk, I will discuss the impact of noise data and imbalanced observations on SVM classification and present a new data adaptive SVM classification method.

Wednesday, November 15, 2017 — 9:00 AM EST

Causal inference in observational data with unmeasured confounding

Observational data introduces many practical challenges for causal inference. In this talk, I will focus on a particular issue when there are unobserved confounders such that the assumption of “ignorability” is violated. For making a causal inference in the presence of unmeasured confounders, instrumental variable (IV) analysis plays a crucial role. I will introduce a hierarchical Bayesian likelihood-based IV analysis under a Latent Index Modeling framework to jointly model outcomes and treatment status, along with necessary assumptions and sensitivity analysis to make a valid causal inference. The innovation in our methodology is an extension of existing parametric approach by i.) accounting for an unobserved heterogeneity via a latent factor structure, and ii.) allowing non-parametric error distributions with Dirichlet process mixture models. We demonstrate utility of our model in comparing effectiveness of two different types of vascular access for a cardio-vascular procedure.

Friday, November 17, 2017 — 9:00 AM EST

A Group-Specific Recommender System

In recent years, there has been a growing demand to develop efficient recommender systems which track users’ preferences and recommend potential items of interest to users. In this article, we propose a group-specific method to use dependency information from users and items which share similar characteristics under the singular value decomposition framework. The new approach is effective for the “cold-start” problem, where, in the testing set, majority responses are obtained from new users or for new items, and their preference information is not available from the training set. One advantage of the proposed model is that we are able to incorporate information from the missing mechanism and group-specific features through clustering based on the numbers of ratings from each user and other variables associated with missing patterns. In addition, since this type of data involves large-scale customer records, traditional algorithms are not computationally scalable. To implement the proposed method, we propose a new algorithm that embeds a back-fitting algorithm into alternating least squares, which avoids large matrices operation and big memory storage, and therefore makes it feasible to achieve scalable computing. Our simulation studies and MovieLens data analysis both indicate that the proposed group-specific method improves prediction accuracy significantly compared to existing competitive recommender system approaches.

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