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Modeling conditional reference regions: Application to glycemic markers.

Óscar Lado-Baleato1, Javier Roca-Pardiñas2, Carmen Cadarso-Suárez1

  • 1Department of Statistics, Mathematical Analysis, and Optimization, Universidade de Santiago de Compostela, Galicia, Spain.

Statistics in Medicine
|August 16, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for estimating bivariate reference regions for diagnostic tests, accommodating non-Gaussian data and patient variables like age. This approach improves the interpretation of multiple test results in clinical decision-making.

Keywords:
conditional reference regionsdiabetesflexible additive predictorskernel smoothingregression

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Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Statistical Modeling

Background:

  • Clinical decisions often rely on diagnostic test results, typically interpreted using univariate reference ranges.
  • Interpreting results from multiple diagnostic tests necessitates bivariate reference regions, which are statistically challenging to model.
  • Existing statistical methods for reference regions often assume Gaussian distributions, limiting their applicability.

Purpose of the Study:

  • To develop a novel statistical method for estimating bivariate reference regions without assuming normal distribution.
  • To incorporate patient-specific covariates, such as age and sex, into the reference region estimation.
  • To provide a more flexible and accurate approach for interpreting results from multiple continuous diagnostic tests.

Main Methods:

  • A nonparametric bivariate location-scale model was employed, utilizing polynomial kernel smoothers within additive models.
  • A backfitting algorithm was used for model estimation, with optimal smoothing parameters selected via cross-validation.
  • The method was validated through simulation studies, particularly under non-Gaussian conditions.

Main Results:

  • The proposed statistical method successfully estimated bivariate reference regions without assuming Gaussian data.
  • The model demonstrated satisfactory performance in simulation studies, even with non-Gaussian data.
  • The methodology proved effective in establishing a reference region for diabetes diagnostic tests (fasting plasma glucose and glycated hemoglobin), considering patient age.

Conclusions:

  • The new nonparametric statistical method offers a robust approach for estimating bivariate reference regions.
  • This method overcomes the limitations of Gaussian assumptions, enhancing the interpretation of multiple diagnostic tests.
  • The approach is valuable for clinical practice, exemplified by its application to diabetes diagnostics incorporating patient age.