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Predicting preference-based utility values using partial proportional odds models.

Roberta Ara1, Ben Kearns, Ben A vanHout

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Partial proportional odds models (PPOM) offer a more accurate method for generating health utility scores compared to ordinary least squares (OLS) regressions. PPOMs better characterize the distribution of EQ-5D data, reducing prediction errors.

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Area of Science:

  • Health Economics
  • Biostatistics
  • Psychometrics

Background:

  • Traditional analyses of utility data predominantly employ ordinary least squares (OLS) regressions.
  • Existing methods may not fully capture the nuances of health-related quality of life data.

Purpose of the Study:

  • To investigate the advantages of using partial proportional odds models (PPOM) for generating preference-based utility scores.
  • To compare PPOMs with OLS regressions in analyzing health utility data.

Main Methods:

  • Utilized EQ-5D data from the Health Survey for England.
  • Estimated PPOMs and OLS regressions, comparing predicted utility scores.
  • Incorporated explanatory variables such as age, health status, and socioeconomic factors.

Main Results:

  • PPOMs more accurately represent the distribution of EQ-5D preference-based utility scores than OLS.
  • PPOMs demonstrated reduced mean absolute and mean squared errors in individual predictions.
  • The models effectively characterized the underlying distributions within the EQ-5D dataset.

Conclusions:

  • Partial proportional odds models provide a superior characterization of EQ-5D data distributions compared to OLS.
  • Further research into conditional and two-part models may offer additional improvements.