Options for Handling Missing Data in the Health Utilities Index Mark 3

Arash Naeim, Emmett B. Keeler and Carol M. Mangione
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Arash Naeim: Division of Hematology-Oncology, UCLA Department of Medicine, Los Angeles, California, anaeim@mednet.ucla.edu
Emmett B. Keeler: RAND School of Public Policy, Division of Health Services Research, Santa Monica, California
Carol M. Mangione: Division of General Internal Medicine and Health Services Research, UCLA Department of Medicine, Los Angeles, California

Medical Decision Making, 2005, vol. 25, issue 2, 186-198

Abstract: Background . The Health Utilities Index Mark 3 (HUI3) is a tool composed of 41 questions, covering 8 attributes: vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain. Responses to these questions can define more than 972,000 health situations. This tool allows respondents to answer “Don’t Know,†for which there is no scoring instruction, to any given question. This situation creates a break in the scoring algorithm and leads to considerable amounts of missing data. The goal of this study is to develop strategies to deal with HUI3 scores for participants who have missing data. Methods . The authors used data from 248 individuals enrolled in the Cataract Management Trial, focusing on the HUI3 vision and ambulation attributes, which had 19% and 10% of attribute levels missing, respectively. Inspection and deduction were used to fill in values independent of the value of the missing data, then alternative analytic techniques were compared, including mean substitution, model scoring, hot deck, multiple imputation, and regression imputation. Results . Inspection and logical deduction reduced the percentage of missing information in the HUI3 by 49% to 87%. A comparison of analytic techniques used for the remaining HUI3 vision data missing demonstrated the value of building models based on internal response patterns and that simple analytic techniques fare as well as more complicated ones when the number of missing cases is small. Conclusion .Analyzing the pattern of responses in cases where the attribute level score is missing reduces the amount of missing data and can simplify the analytic process for the remaining missing data.

Keywords: health utilities index; missing data; non-response; pattern analysis; imputation (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:25:y:2005:i:2:p:186-198

DOI: 10.1177/0272989X05275153

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