Literature DB >> 29753360

Prediction of Health Utility Scores in Patients with Chronic Hepatitis C Using the Chronic Liver Disease Questionnaire-Hepatitis C Version (CLDQ-HCV).

Maria Stepanova1, Issah Younossi1, Andrei Racila1, Zobair M Younossi2.   

Abstract

BACKGROUND: Preference-based health utilities are used in economic analyses of disease burden and health care interventions. When specifically designed instruments cannot be applied, mapping algorithms for non-preference-based instruments can be used for prediction of health utility scores.
OBJECTIVES: To develop a mapping algorithm for the Chronic Liver Disease Questionnaire-Hepatitis C Version (CLDQ-HCV), the hepatitis C virus-specific quality-of-life instrument.
METHODS: We used a sample of patients with HCV who completed the short form 36 health survey and the CLDQ-HCV in clinical trials; six-dimensional health state short form (SF-6D) utilities were derived from the 36-item short form health survey. Regression models with components of the CLDQ-HCV being predictors and SF-6D being the outcome were developed and tested in an independent testing set and in clinically significant subpopulations.
RESULTS: The sample of 34,822 records was split (4:1) into training and testing set. Simple mixed models had a root mean square error up to 0.088; predicted and observed utilities were highly correlated (Pearson correlation 0.81-0.82) although predicted utilities were underestimated in the range closest to perfect scores. Generalized linear models had better average accuracy (root mean square error up to 0.0839; correlations up to 0.844) and significantly better accuracy in the highest values (median error up to 0.065). Accuracy in the independent testing set was nearly identical, and so was accuracy in patients with compensated and decompensated cirrhosis; the errors of group means were less than 0.015.
CONCLUSIONS: A number of linear models for mapping domains or items of CLDQ-HCV to SF-6D health utilities have been developed. The models have excellent accuracy at the group level. Predicted health utility scores can be used in further economic analyses involving patients with HCV.
Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  QALY; SF-6D; clinical trial; prediction; regression

Mesh:

Year:  2017        PMID: 29753360     DOI: 10.1016/j.jval.2017.10.005

Source DB:  PubMed          Journal:  Value Health        ISSN: 1098-3015            Impact factor:   5.725


  2 in total

1.  Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease.

Authors:  Michael Onyema Edeh; Surjeet Dalal; Imed Ben Dhaou; Charles Chuka Agubosim; Chukwudum Collins Umoke; Nneka Ernestina Richard-Nnabu; Neeraj Dahiya
Journal:  Front Public Health       Date:  2022-04-27

2.  Cost-utility analysis of four WHO-recommended sofosbuvir-based regimens for the treatment of chronic hepatitis C in sub-Saharan Africa.

Authors:  Sylvie Boyer; Maël Baudoin; Marie Libérée Nishimwe; Melina Santos; Maud Lemoine; Gwenaëlle Maradan; Babacar Sylla; Charles Kouanfack; Patrizia Carrieri; Abbas Mourad; Nicolas Rouveau; Raoul Moh; Moussa Seydi; Alain Attia; Maame Esi Woode; Karine Lacombe
Journal:  BMC Health Serv Res       Date:  2022-03-05       Impact factor: 2.655

  2 in total

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