Addressing Ceiling Effects in Health Status Measures: A Comparison of Techniques Applied to Measures for People with HIV Disease

Huang, I‐Chan; Frangakis, Constantine; Atkinson, Mark J.; Willke, Richard J.; Leite, Walter L.; Vogel, William; Wu, Albert W.

doi:10.1111/j.1475-6773.2007.00745.x

Cited by 108 publications

(133 citation statements)

References 19 publications

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“…Similar findings have been shown to apply to models like the Tobit 21 (designed to deal with limited dependent variables), two-part models 22 (which attempt to address the mass of observations seen at full health) and censored least absolute deviations models 23,24 . A common finding in those reports is that expected health utility associated with mild health states is underestimated whilst utility for more severe health states is overestimated.…”

Section: Selection Of the Statistical Modelmentioning

confidence: 67%

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

et al. 2017

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ACKNOWLEDGMENTSThe individual contributions of Sorrel Wolowacz and Richard Willke are gratefully acknowledged. We thank all reviewers who commented during our forums at the ISPOR Milan and Amsterdam European Congresses. We especially thank the following individuals who reviewed drafts of the report and submitted written comments. Their feedback has both improved the manuscript and made it an expert consensus ISPOR task force report. Many thanks to Tony Ades, René Allard, Gouri Shankar Bhattacharyya, Lance Brannman, Michael Carter, David Cella, Akriti Chahar, Stephanie Yanjing Chen, Shiva Devarakonda, Salah Ghabri, Linda Gore Martin, Inigo Gorostiza, Thomas Grochtdreis, Michael Hagan, Nadine Hillock, Cynthia Holmes, Shrividya Iyer, Steve Kay, Jeanette Kusel, Ramanath KV, Dawn Lee, Joanna Le niowska, Sophia E. Marsh, Alan Martin, Nicholas Mitsakakis, Sharanya Murty, Nneka Onwudiwe, Guilhem Pietri, G.M. Rabiul Islam, Ghabri Salah, Mihail Samnaliev, Carsten Schousboe, Sarah Shingler, Fatema Turkistani, and Uday Venkat. Finally, many thanks to Theresa Tesoro for her assistance in developing this task force report. 2 AbstractEconomic evaluation conducted in terms of cost per Quality Adjusted Life Year (QALY) provides information that decision makers find useful in many parts of the world. Ideally, clinical studies designed to assess the effectiveness of health technologies would include outcome measures that are directly linked to health utility in order to calculate QALYs. Often this does not happen and, even where it does, clinical studies may be insufficient for a cost-utility assessment. Mapping can solve this problem. It uses an additional dataset to estimate the relationship between outcomes measured in clinical studies and health utility. This bridges the evidence gap between available evidence on the effect of a health technology in one metric and the requirement for decision makers to express it in a different one (QALYs). In 2014, ISPOR established a Good Practices for Outcome Research Task Force for mapping studies. This Task Force Report provides recommendations to analysts undertaking mapping studies, those that use the results in cost utility analysis, and those that need to critically review such studies. The recommendations cover all areas of mapping practice: the selection of datasets for the mapping estimation, model selection and performance assessment, reporting standards, and the use of results including the appropriate reflection of variability and uncertainty. This report is unique because it takes an international perspective, is comprehensive in its coverage of the aspects of mapping practice, and reflects the current state of the art.

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Section: Selection Of the Statistical Modelmentioning

confidence: 67%

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

et al. 2017

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“…The published methods for regression analysis of the EQ-5D index include OLS model (Fryback et al 2007), Tobit model, CLAD model (Clarke et al 2002;Sullivan et al 2005;Sullivan and Ghushchyan 2006), and the latent class model (Huang et al 2008). The OLS models the conditional mean of the EQ-5D index as a linear function of the covariates.…”

Section: Analytical Issues Of Analyzing Eq-5d Indexmentioning

confidence: 99%

Some methodological issues with the analysis of preference-based EQ-5D index score

2009

Health Serv Outcomes Res Method

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The EQ-5D index is widely used to assess the preference-based health status. In this paper, we examine the analytical issues of regression models for the US preferencebased EQ-5D index score. We propose a two-part approach to model the special features of the EQ-5D index. The first part is a logistic model for the probability of reaching the maximum score 1.0. The second part is a model for the rest of the scores that are less than 1.0, which can be a least squares regression with robust standard errors for the conditional mean, or a quantile regression for conditional quantiles such as the median. We show that the two-part model has some desirable features that are not available in the previously published regression methods for the EQ-5D index. We illustrate the proposed approach with data from the Medical Expenditure Panel Survey. The proposed method may be used for other utility or health related quality of life scores of similar features.

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“…The second part explains the expectation of the score given that some health problem has been reported by means of a Generalized Linear Model (GLM) with a logarithmic link and gamma disturbances. The TPM has been shown to produce good results in terms of predictive power in comparison with other models in the context of the EQ-5D-5L [19]. Also, the TPM is readily interpretable.…”

Section: Ourmentioning

confidence: 82%

“…Similarly, the differences between males and females discussed above call for a separate analysis for both genders. Among the various statistical alternatives suggested in the literature we opt for the Two Part Model (TPM) [19]. The first part of the model estimates the probability of reporting the maximum score (i.e.…”

Section: Ourmentioning

confidence: 99%

Smoking, health-related quality of life and economic evaluation

2017

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Addressing Ceiling Effects in Health Status Measures: A Comparison of Techniques Applied to Measures for People with HIV Disease

Abstract: The LCM and TPM with a log-transformed dependent variable are superior to other approaches in handling data with ceiling effects.

Cited by 108 publications

References 19 publications

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

Mapping to Estimate Health-State Utility from Non–Preference-Based Outcome Measures: An ISPOR Good Practices for Outcomes Research Task Force Report

Some methodological issues with the analysis of preference-based EQ-5D index score

Smoking, health-related quality of life and economic evaluation

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