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Test Methods for Missing at Random Mechanism in Clustered Data

Abstract Number:

1920 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Haoyu Zhou (1), Recai Yucel (1), Resa M. Jones (2), Edoardo Airoldi (1)

Institutions:

(1) Temple University, N/A, (2) Temple University, Department of Epidemiology & Biostatistics, Philadelphia, PA

Co-Author(s):

Recai Yucel  
Temple University
Resa M. Jones  
Temple University, Department of Epidemiology & Biostatistics
Edoardo Airoldi  
Temple University

First Author:

Haoyu Zhou  
Temple University

Presenting Author:

Haoyu Zhou  
Temple University

Abstract Text:

Methods dealing with missing data rely on assumptions underlying the missing data whether they are explicit or implicit.
For example, one of the most popular such method, multiple imputation (MI), typically assumes missing at random (MAR) in its most implementations, even though the theory of MI does not necessarily require MAR. In this work, we consider formal tests for condition known as missing always at random (MAAR) as a way to explore MAR mechanism in settings where observational units are nested within naturally occurring groups. Specifically, we propose two tests for MAR mechanisms that extend existing methods to incorporating clustered data structures: 1) comparison of conditional means (CCM) with clustering effects and 2) testing a posited missingness mechanism with clustering effects. We design a simulation study to evaluate the tests' performance in correctly capturing the missingness mechanism and demonstrate their use in a real-word application on post-Covid conditions that utilizes an EHR dataset. These test methods are expected to provide empirical evidence for improved selection of missing data approaches in application.

Keywords:

MAR|missingness mechanism|test|clustering effects| |

Sponsors:

Biopharmaceutical Section

Tracks:

Missing Data

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