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Predicting EQ-5D utility scores from the 25-item National Eye Institute Vision Function Questionnaire (NEI-VFQ 25) in patients with age-related macular degeneration

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Abstract

Purpose

In this study, we explored different statistical approaches to identify the best algorithm to predict EQ-5D utility scores from the NEI-VFQ 25 in patients with age-related macular degeneration (AMD).

Methods

Ordinary least squares (OLS), Tobit, and censored least absolute deviation (CLAD) approaches were compared using cross-sectional data (primary dataset, n = 151) at screening from a phase I/II clinical trial in patients with AMD. Three models were specified in this study: full (includes all 12 dimensions of the NEI-VFQ 25), short (includes only the general health dimension and the composite score), and reduced model (using stepwise regression). To evaluate the predictive accuracy of the models, the mean absolute prediction error (MAPE), mean error, and root means squared error were calculated using in-sample cross-validation (within the primary dataset) and out-of-sample validation using an independent dataset (n = 393). The model that provided the lowest prediction errors was chosen as the best model.

Results

In-sample cross-validation and out-of-sample validation consistently demonstrated that, compared to other approaches, heteroscedasticity-adjusted OLS produced the lowest MAPE (mean values were 0.1400, 0.1593, respectively) for the full model, while CLAD performed best for the short and reduced models (mean values were 0.1299, 0.1483, respectively). The normality and homoscedasticity assumptions of both OLS and Tobit were rejected. CLAD, however, can accommodate these particular violations.

Conclusions

The CLAD-short model is recommended for producing the EQ-5D utility scores when only the NEI-VFQ 25 data are available.

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Financial disclosures

This study was part of a research contract to Purdue University (Kent H. Summers, PI, a former employee) funded by Pfizer Inc. Nalin Payakachat received a graduate research assistantship from this contract through Purdue University.

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Authors and Affiliations

  1. Division of Pharmaceutical Evaluation and Policy, Department of Pharmacy Practice, College of Pharmacy, University of Arkansas for Medical Sciences, EDIII slot#522-4, 4301 W. Markham St., Little Rock, AR, 72205, USA

    Nalin Payakachat

  2. Health Outcomes and PharmacoEconomics (HOPE), Endo Pharmaceuticals Inc., 100 Endo Boulevard, Chadds Ford, PA, 19317, USA

    Kent H. Summers

  3. Worldwide Medical and Outcomes Research, Pfizer Global Pharmaceuticals, Pfizer, Inc., 10555 Science Center Drive (CB10), San Diego, CA, 92121, USA

    Andreas M. Pleil

  4. Department of Pharmacy Practice, Purdue University School of Pharmacy, R. Heine Pharmacy Bldg., Rm. 502, 575 Stadium Mall Drive, West Lafayette, IN, 47907-2091, USA

    Matthew M. Murawski & Joseph Thomas III

  5. Department of Statistic, Purdue University, 150 N. University Street, West Lafayette, IN, 47907-2067, USA

    Kristofer Jennings

  6. Department of Sociology & Anthropology, Purdue University, 700 West State Street, West Lafayette, IN, 47907-2059, USA

    James G. Anderson

Authors
  1. Nalin Payakachat
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  2. Kent H. Summers
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  3. Andreas M. Pleil
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Correspondence to Nalin Payakachat.

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Payakachat, N., Summers, K.H., Pleil, A.M. et al. Predicting EQ-5D utility scores from the 25-item National Eye Institute Vision Function Questionnaire (NEI-VFQ 25) in patients with age-related macular degeneration. Qual Life Res 18, 801–813 (2009). https://doi.org/10.1007/s11136-009-9499-6

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