There is a big difference between thinking a policy or a treatment is effective and knowing it will have an impact — and that can make a big difference when multi-billion dollar health care companies and governments are looking for effective policies to put into place.
Dr. Bo Lu, associate professor in The Ohio State University College of Public Health Division of Biostatistics, recently received an R01 award from the Agency for Healthcare Research and Quality (AHRQ) for a project titled “Causal inference for treatment effect using observational health care data with unequal sampling weights.” Dr. Lu is teaming up with Dr. Huiyun Xiang, professor of medicine from Nationwide Children’s Hospital to develop a new method that can help healthcare leaders determine if their policies and practices are really improving outcomes.
“One of the major resources that we use to make decisions are survey data, but those major population health surveys come with a very complex design,” said Dr. Lu. “We are now developing a new method to combine the complex survey design with the statistical techniques for causal inference. This will help us obtain accurate and reliable information on the effects.”
Knowing a program or policy has the desired outcome is a critical part of evidenced-based health care programs. Because different groups of people may respond differently to a policy change or the introduction of a certain medical treatment, the new method is needed to clarify if the intervention did indeed have an impact in the targeted population after adjusting for survey design and controlling for confounders.
“We are delighted that Dr. Bo Lu and my team will collaborate again in this significant project,” said Dr. Xiang, director of pediatric trauma research at the Research Institute at Nationwide Children’s Hospital and the co-PI of the funded project. “Patient-centered outcome evaluation and research need a new approach for causal treatment inference based on large observational medical or survey data and we aim to develop such a method and make it widely available to researchers.”
The proposed study will achieve four specific aims: (1) develop a potential-outcome-based theoretical framework to streamline causal inference in complex surveys; (2) develop both propensity score and survey design adjusted estimators, including weighted, stratified and matched estimators; (3) conduct extensive simulation studies to evaluate the performance of various estimators under different practical scenarios and develop a statistical software package for practitioners; and (4) apply the proposed methodology to a real survey for comparative trauma care research. This study is expected to fill a critical gap in health care policy and treatment effect evaluation research by extending the commonly used propensity score adjustment for non-survey data to complex sampling designs. Findings of this study will help promote AHRQ’s mission to produce more accurate evidence for health care program evaluation and to improve the current practice of comparative health outcome research.
“A significant contribution is this general purpose methodology which will be widely applicable and can benefit government agencies, policy makers, and social, political and health science researchers, in those situations where survey data are vital sources for comparative outcomes research and program policy evaluation,” added Dr. Lu. Read more >>