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Activity Number: 233 - Innovations in Inferential and Design Strategies in Mental Health Research
Type: Contributed
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Mental Health Statistics Section
Abstract #322579
Title: The Role of Intermediate Observations When Outcome Data Are Missing in Longitudinal Randomized Clinical Trials
Author(s): Joseph Rausch*
Companies: Nationwide Children's Hospital
Keywords: randomized clinical trials ; missing data ; pre-post designs ; longitudinal designs ; mixed models ; missing data mechanisms
Abstract:

Intention-to-treat (ITT) is widely considered the recommended approach for analyzing primary outcomes in randomized clinical trials, but can yield biased treatment effects in the presence of missing outcome data (Little & Kang, 2015, Statistics in Medicine). One option that requires more research for obtaining accurate treatment effect estimates when outcome data are missing is the use of outcome data obtained at intermediate time points between the pretest and the posttest. The current study examines how such data may be used analytically to reduce bias and increase precision of treatment effects in RCTs when outcome data are missing. Various missing data properties (e.g., MAR vs MNAR missing data mechanisms, differential drop-out types), analytic approaches (e.g., pre-post ANCOVA, mixed-model approaches which allow MAR data), and longitudinal RCT designs with intermediate observations are examined. Practical recommendations are provided to researchers concerning when it is worthwhile to employ a research design with intermediate observations to obtain more accurate inference and estimates for treatment effects in RCTs when outcome data are missing.


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

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