Bias in Area Under the Curve for Longitudinal Clinical Trials With Missing Patient Reported Outcome Data

Melanie L. Bell, Madeleine T. King and Diane L. Fairclough

SAGE Open, 2014, vol. 4, issue 2, 2158244014534858

Abstract: A common approach to the analysis of longitudinal patient reported outcomes (PROs) is the use of summary measures such as area under the time curve (AUC). However, it is not clear how missing data affects the validity of AUC analysis. This study aimed to compare the use of AUC summary measures (in individuals) with AUC summary statistics (on groups, calculated from the estimated parameters of a mixed model) when data are complete, missing at random, and missing not at random. A simulation experiment based on a two-armed randomized trial was carried out to investigate the precision and bias of AUC in longitudinal analysis where missingness, trajectory, and missingness allocation were varied. Summary measures AUC with ad hoc approaches to missing data were compared with mixed model AUC summary statistics. AUC summary statistics were consistently superior to AUC summary measures in terms of precision and bias. The bias of AUC summary statistic approach was very small, even when data were missing not at random and when differential attrition between groups existed. AUC summary measures on individuals should not be used to analyze longitudinal PRO data in the presence of missing data.

Keywords: health psychology; applied psychology; psychology; social sciences; research methods; statistical theory and tests; reliability and validity; data processing and interpretation (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:4:y:2014:i:2:p:2158244014534858

DOI: 10.1177/2158244014534858

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