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Generalized Boosted Models to Measure Racial Effects at Different Quantiles in Observational Studies.

Lili Yue1, Jiayue Zhang2, Ping Yu3

  • 1School of Statistics and Data Science, Nanjing Audit University, Nanjing, China.

Biometrical Journal. Biometrische Zeitschrift
|June 23, 2025
PubMed
Summary

This study introduces a new method to estimate treatment effects at various quantiles using longitudinal data. The research found that racial effects on cardiovascular risk factors differ across quantile levels.

Keywords:
generalized boosted modelsinverse probability weightinglongitudinal dataobservational studiesquantile treatment effect

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Observational studies with longitudinal data present challenges in estimating treatment effects, particularly at different quantiles.
  • The National Heart, Lung, and Blood Institute (NHLBI) Growth and Health Study (NGHS) provides a relevant dataset for examining racial effects on cardiovascular risk factors over time.

Purpose of the Study:

  • To develop and evaluate a novel estimation method for quantile treatment effects in observational longitudinal studies.
  • To assess racial disparities in cardiovascular risk factors using the NGHS data by estimating effects at various quantiles.

Main Methods:

  • Employed a nonparametric generalized boosted models (GBM) approach to estimate unknown propensity score models.
  • Developed a GBM-based quantile weighting estimation method by integrating quantile regression and inverse probability weighting.
  • Applied the proposed method to NGHS data to quantify racial effects across different quantile levels.

Main Results:

  • The study found that racial effects on cardiovascular risk factors are not uniform and vary significantly across different quantile levels.
  • Results indicate that racial effects may be non-zero, highlighting potential disparities that are missed by traditional average treatment effect analyses.

Conclusions:

  • The proposed GBM-based quantile weighting method is effective for estimating quantile treatment effects in longitudinal observational studies.
  • The findings underscore the importance of considering quantile-specific effects to fully understand complex relationships, such as race and cardiovascular health.