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Activity Number: 241 - SPEED: Statistics in Social Sciences and Survey Research Part 1
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
Sponsor: Health Policy Statistics Section
Abstract #322291
Title: Comparing Methods for Weighting to Extend Inferences from a Collection of Trials
Author(s): Nicole Schnitzler* and Eloise E Kaizar
Companies: The Ohio State University and The Ohio State University
Keywords: Causal Inference; Meta-analysis; Randomized Controlled Trials; Weighting; Transportability ; Generalizability
Abstract:

When we are interested in the effect of a treatment will have in a specific target population, it is unlikely that we will have access to a randomized clinical trial (RCT) whose participants are a random sample from that population. Because RCTs often lack the ability to naturally generalize to populations outside the population of study participants, obtaining causally interpretable estimates of the treatment effect in our target population can be challenging. When multiple RCTs on a treatment are available, we may be able to obtain a causally interpretable estimate of the treatment effect in the target population by using weights to synthesize participant data across the trials. Existing weighting methods construct weights by first pooling across studies, treating the data as if it came from one large study instead of several smaller ones. An alternative weighting method accounts for study-membership through the use of participant-level and study-level weights. We examine the performance of the different weighting methods using simulations, highlighting settings where different methods provide the most favorable statistical properties.


Authors who are presenting talks have a * after their name.

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