Talk by Huiting Ma (11am - 11:30am)
Title: The Impact of HCV Co-Infection on Healthcare-Related Utilization among HIV Patients in British Columbia, Canada
Abstract: Over the past several decades, the analysis of healthcare-related count data has been increasingly recognized as an essential research topic for many researchers. There is a long history of analyzing counts data under the framework of parametric distributions for independent identically distributed random variables. Standard methodology for analysing this type of data falls under generalized linear models, which include Poisson regression models and negative binomial regression models. There are other alternative models, such as quasi-Poisson and zero-inflated models. Analysis of longitudinal count data, which contain repeated count observations for each subject over time, is often done by fitting generalized estimating equation models and generalized mixed effects models.
In this project, our main objective was to characterize the trends in healthcare utilization (i.e. the number of healthcare related visits) of HIV mono-infected individuals and HIV/hepatitis C (HCV) co-infected individuals, in British Columbia, from April 1, 1997 to March 31, 2010. We use several statistical methods for the analysis of healthcare-related cross-sectional and longitudinal count data. Understanding the healthcare burden among HIV mono-infected and HIV/HCV co-infected individuals has the potential to help stakeholders to identify and address the unique healthcare needs of these individuals. Our data analyses results show that individuals with an HIV/HCV co-infection status were at a risk of experiencing higher rates of healthcare-related visits than HIV mono-infected individuals.
Talk by Chiara Di Gravio (11:30am - 12pm)
Title: Instrumental Variables Selection: a Comparison between Regularization and Post-Regularization Methods
Abstract: Instrumental variables are commonly used in statistics, econometrics, and epidemiology for the estimation of causal effects when controlled experiments are not available. Specifically, instrumental variables estimators provide consistent parameter estimates in regression models when some of the predictors are correlated with the error term. However, the properties of these estimators are sensitive to the choice of valid instruments. Since in many applications, valid instruments come in a bigger set that includes also weak and possibly irrelevant instruments, the researcher needs to select a smaller subset of instruments that are relevant and strongly correlated with the predictors in the model.
In this project we review the instrumental variables estimators, discuss the problems related to having instruments that are either weak or possibly irrelevant instruments, and compare already existing techniques with new approaches. Simulation studies will be presented to compare the different methods.