The obesity epidemic will affect 1 in 2 adults in the US by 2030. While several medical and surgical weight
reduction strategies are available, with newer, transformative medications recently approved, little is known
about their safety and effectiveness in clinical practice. Real-world evidence (RWE) studies based on routinely
generated health insurance claims from millions of patients have the size and the breadth of data capture
needed to provide critical information on the safety and effectiveness of weight loss strategies in clinical
practice, complementing evidence from RCTs. In this setting, however, the adiposity measure that guides
weight loss treatment, i.e., body mass index (BMI), although captured with high specificity, is often missing.
Yet, BMI information is necessary to accurately identify study populations, adjust for confounding, and assess
effect modification on a large scale in claims data. Similarly, confounding and other biases may be present
in RWE studies. Thus, identifying methodological approaches that increase the validity of RWE studies
comparing weight loss strategies is critical to generate unbiased findings that can validly address questions not
answered or answerable by RCTs. Our overarching goal is to develop, implement, and test approaches
to produce large scale, high-quality RWE on the comparative safety and effectiveness of medical and
surgical weight loss strategies to complement RCTs. To achieve this goal, we will leverage (1) the Nurses’
Health Studies (NHS) I and II and the Health Professionals Follow-up Study (HPFS), large longitudinal cohort
studies with rich lifestyle and dietary data, linked with their insurance claims data, and (2) large U.S. national
(federal and commercial) claims databases, resulting in a heretofore untapped new data infrastructure. We will
improve existing algorithms to measure BMI in national claims data using primary data from longitudinal cohort
studies (NHS I and II, and HPFS) linked with their claims data (Aim 1); assess the success of approaches to
reduce confounding, including comparator choice and propensity score adjustment, with respect to measures
of achieved comparability between medical and surgical weight loss strategies identified in claims-based RWE
studies with regard to information only measured in the linked data (Aim 2); evaluate when RWE studies of
weight reduction strategies emulating target trials provide causal conclusions (Aim 3); and fill gaps in the RCT
evidence in targeted questions regarding the safety and effectiveness of medical and surgical weight reduction
strategies in clinical practice (Aim 4). This work will develop more accurate, methodologically rigorous
approaches to conduct RWE studies of weight reduction strategies using large scale, real-world data
and will create an invaluable infrastructure to conduct studies on the comparative safety and
effectiveness of various cardiometabolic treatments.
