Estimation of Health-Related Quality of Life in the Presence of Missing Values in EQ-5D

Muhammad Atif,Muhammad Shafiq,Abdel-Salam G Abdel-Salam,Qamruz Zaman,Ishfaq Ahmad

doi:10.1155/2021/9145191

Abstract

One of the notes worthy problems in analysis of clinical and observational studies is missing data and nonresponse from patients. Turning a blind eye to the missing behavior may provide biased results with overestimated standard errors. The potential impact of the problem may even have more severe impression in estimating health-related quality of life index. This index is an important indicator, widely used in clinical trials for assessing effectiveness of available interventions. Amongst many available measures for estimation of the index, the most rising approach is the EQ-5D preference-based health classifier. This study suggests a cluster-based heuristic algorithm for imputation of missing values in the EQ-5D health classifier to overcome the said problem. The use of auxiliary variable and other dimension’s values as evidences increases the chance of correct identification of the missing value and hence makes it unbiased. Comparisons of bootstrap samples suggest that it overcomes the problem of standard errors and provides efficient estimates.

Highlights

Provision of medical intervention and clinical facilities on an affordable expense to population is one of the prime goals of public health policy and practice
In addition to the EQ-5D classifier, the valuation of Health-related quality of life (HRQol) comprises an optical scale as well, usually the visual analogue scale (VAS) or timetrade-off (TTO) scale. Valuations of this visual scale are regressed on the EQ-5D health state vector, and HRQol index is estimated from regression coefficients. e indexbased score is typically interpreted along a continuum, where 1 represents the best and 0 represents the worst possible health state [10, 11]
This study aims to study the impact of missing in the EQ-5D health classifier on HRQol index and suggest a technique for imputation that can overcome the problem

Summary

Introduction

Provision of medical intervention and clinical facilities on an affordable expense to population is one of the prime goals of public health policy and practice. For this purpose, public health officials use cost-effective ratio to measure the consequence of intervention on physical and mental health of individuals, as well as the additional cost to be paid for improved health conditions. Each dimension of the classifier is presented on the questionnaire with three ordinal levels of responses, i.e., no problem, some/ moderate problem, and extreme problem [8, 9] In this way, the EQ-5D self-classifier provides 243 different possible categories of the health profile. Valuations of this visual scale are regressed on the EQ-5D health state vector, and HRQol index is estimated from regression coefficients. e indexbased score is typically interpreted along a continuum, where 1 represents the best and 0 represents the worst possible health state [10, 11]

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Methods

Conclusion