Analysis of Clinical Trials with Missing Data: Robust Alternatives to Standard Methods
View Presentation *Devan Mehrotra, Merck Research Laboratories Xiaoming Li, Merck Research Laboratories Jiajun Liu, Merck Research Laboratories Kaifeng Lu, Merck Research Laboratories Keywords: In a typical comparative clinical trial, the longitudinal (baseline and post-baseline) data, possibly incomplete due to dropouts, are commonly analyzed using likelihood-based methods (e.g., REML) that assume multivariate normality of the response vector, conditional on potential covariates. If the normality assumption is untenable, semi-parametric methods like generalized estimating equations (GEE) or weighted GEE (WGEE) are sometimes used. With a goal of retaining good performance under normality, but significantly increasing efficiency under non-normality, we present alternate approaches that involve multiple imputation of missing values followed by an outlier-resistant parametric analysis, all easily implemented using existing SAS procedures. The efficiency gains using the new methods versus REML, GEE and WGEE are quantified using simulations, and illustrated using a real example.
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Key Dates
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April 30 - May 22, 2013
Invited Abstract Submission Open -
June 4, 2013
Online Registration Opens -
August 9 - August 23, 2013
Invited Abstract Editing -
August 23, 2013
Short Course materials due from Instructors -
August 26, 2013
Housing Deadline -
September 9, 2013
Cancellation Deadline and Registration Closes @ 11:59 pm EDT -
September 16 - September 18, 2013
Marriott Wardman Park, Washington, DC