Probabilistic Mapping of Descriptive Health Status Responses Onto Health State Utilities Using Bayesian Networks
An Empirical Analysis Converting SF-12 Into EQ-5D Utility Index in a National US Sample
Bibliographic Data
| ID | 9101268 |
|---|---|
| Authors | Quang A Le (0000-0002-4968-9334, Western University of Health Sciences, corresponding author), Jason N Doctor (0000-0003-3661-8745, University of Southern California) |
| Year | 2011 |
| Volume | 49 |
| Issue | 5 |
| Pages | 451-460 |
| Publication date | 2011-05-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0b013e318207e9a8 |
| PMID | 21422961 |
| OpenAlex | W2036067016 |
| Language | EN |
| Citations received | 9 |
BACKGROUND: As quality-adjusted life years have become the standard metric in health economic evaluations, mapping health-profile or disease-specific measures onto preference-based measures to obtain quality-adjusted life years has become a solution when health utilities are not directly available. However, current mapping methods are limited due to their predictive validity, reliability, and/or other methodological issues. OBJECTIVES: We employ probability theory together with a graphical model, called a Bayesian network, to convert health-profile measures into preference-based measures and to compare the results to those estimated with current mapping methods. METHODS: A sample of 19,678 adults who completed both the 12-item Short Form Health Survey (SF-12v2) and EuroQoL 5D (EQ-5D) questionnaires from the 2003 Medical Expenditure Panel Survey was split into training and validation sets. Bayesian networks were constructed to explore the probabilistic relationships between each EQ-5D domain and 12 items of the SF-12v2. The EQ-5D utility scores were estimated on the basis of the predicted probability of each response level of the 5 EQ-5D domains obtained from the Bayesian inference process using the following methods: Monte Carlo simulation, expected utility, and most-likely probability. Results were then compared with current mapping methods including multinomial logistic regression, ordinary least squares, and censored least absolute deviations. RESULTS: The Bayesian networks consistently outperformed other mapping models in the overall sample (mean absolute error=0.077, mean square error=0.013, and R overall=0.802), in different age groups, number of chronic conditions, and ranges of the EQ-5D index. CONCLUSION: Bayesian networks provide a new robust and natural approach to map health status responses into health utility measures for health economic evaluations
Algorithm · Bayesian network · Bayesian probability · Descriptive statistics · Econometrics · Probabilistic logic · State (computer science) · Statistical model · Statistics · Artificial Intelligence · Computer Science · Economic and Environmental Valuation · Global Health Care Issues · Health Systems, Economic Evaluations, Quality of Life · Mathematics
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| Citations per year | 0.64 |
|---|---|
| Citation span | 2012 - 2024 (13) |
| Highly cited | No |