Speaker 1: Wenxin Lu

Title

Application of Propensity Score Matching to the Americans' Changing Lives Study

Abstract

Randomization makes subjects comparable between exposed and unexposed groups not only on observed baseline characteristics but also on unobserved baseline characteristics. However, randomization is not always feasible because of time and budget constraints, along with ethical considerations, which can bring biased effect estimates. Propensity score (PS) was introduced by Rosenbaum and Rubin (1983), and was used for controlling for selection bias to have better effect estimates in cohort studies. It is one of the most popular methods, and matching, stratification and covariate adjustment on propensity scores are three commonly used techniques. This project aims assess the performance of propensity score matching. An empirical study using Americans' Changing Lives (ACL) data was given to demonstrate the application. Caliper matching without replacement was used to balance nonequivalent groups to get more accurate estimates of the effects of smoking group on corresponding disease. A discussion of limitation was presented at the end of the study.

Speaker 2: Mina Hosseini

Title

Generalized Linear Models for Data with Direct and Proxy Observations

Abstract

In this project, we review three different approaches for the statistical analysis of data sets with monotone missing data patterns. Such patterns occur in epidemiological data sets when the patients become unable to provide responses by themselves due to advancing severity of their conditions. It is common to use proxy responses by a relative or caregiver in these cases. We are investigating statistical models which can incorporate patient or proxy observations, or in some cases both, so that relevant parameters and their standard errors can be estimated in a single framework. We also compare our own weighted GEE code to an experimental SAS procedure, PROC GEE, and discuss possible improvements.