A0649
Title: Leveraging multi-study, multi-outcome data to improve efficiency of clinical trials for opioid use disorder
Authors: Caleb Miles - Columbia University (United States) [presenting]
Amy Pitts - Columbia University (United States)
Rachael Ross - Columbia University (United States)
Soohyun Kim - Weill Cornell Medicine (United States)
Ngoc Duong - Northwestern University (United States)
Oliver Hines - London School of Hygiene and Tropical Medicine (United Kingdom)
Abstract: As data sources have become more plentiful and readily accessible, the practice of data fusion has become increasingly ubiquitous. However, when the focus is on a causal effect on a particular outcome, a major limitation is that this outcome may not be available in all data sources. In fact, different randomized experiments or observational studies of a common exposure will often focus on potentially related yet distinct outcomes. One such example is the medication for opioid use disorder (MOUD) clinical trials network (CTN), which consists of several randomized trials of the comparative effectiveness of different MOUDs with inconsistent quality of life measures across studies. The causally principled methodology is developed for fusing data sets when multiple outcomes are observed across studies, which leverages mediators and outcomes of secondary interest as informative proxies for the missing outcome of primary interest, thereby maximizing power and efficiency by making full use of the available data. As this methodology relies on a key transportability assumption, methods are also developed to assess the degree of sensitivity to violations of this assumption. This methodology is applied to data from the CTN trials to make improved causal inferences about the comparative effectiveness of medications for opioid use disorder.
