Optimizing Variance-Bias Trade-off in the TWANG Package for Estimation of Propensity Scores.

While propensity score weighting has been shown to reduce bias in treatment effect estimation when selection bias is present, it has also been shown that such weighting can perform poorly if the estimated propensity score weights are highly variable. Various approaches have been proposed which can reduce the variability of the weights and the risk of poor performance, particularly those based on machine learning methods. In this study, we closely examine approaches to fine-tune one machine learning technique (generalized boosted models [GBM]) to select propensity scores that seek to optimize the variance-bias trade-off that is inherent in most propensity score analyses. Specifically, we propose and evaluate three approaches for selecting the optimal number of trees for the GBM in the twang package in R. Normally, the twang package in R iteratively selects the optimal number of trees as that which maximizes balance between the treatment groups being considered. Because the selected number of trees may lead to highly variable propensity score weights, we examine alternative ways to tune the number of trees used in the estimation of propensity score weights such that we sacrifice some balance on the pre-treatment covariates in exchange for less variable weights. We use simulation studies to illustrate these methods and to describe the potential advantages and disadvantages of each method. We apply these methods to two case studies: one examining the effect of dog ownership on the owner's general health using data from a large, population-based survey in California, and a second investigating the relationship between abstinence and a long-term economic outcome among a sample of high-risk youth.

Developing a Strategic Program for Chilean Health Information Technology: Environmental Scan and Key Informant Interviews.

As part of an effort to assist Chile in developing a strategic program to foster the development of the health information technology (health IT) sector over the next five to ten years, this study assesses the current state of health IT adoption and implementation in Chile, as well as the challenges and opportunities facing the sector over the coming years. The authors conducted an environmental scan and ten key informant interviews and found that there are a number of successful health IT projects and strategies for further development currently underway in Chile, but that the successful projects are generally localized within specific health care providers and lack integration. These and other challenges suggest significant potential for the Ministry of Economy and other stakeholders to take specific actions designed to encourage further development of the health IT sector in Chile over the coming years. The next phase of this effort will use the results from this study to develop a roadmap for the Ministry of Economy to encourage health IT development in Chile over the short, medium, and long terms.