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Natural interpretations in Tobit regression models using marginal estimation methods.

Wei Wang1, Michael E Griswold1

  • 1Center of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, MS, USA.

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|September 3, 2015
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Summary
This summary is machine-generated.

This study introduces new methods for analyzing censored data, improving the estimation of exposure effects on the actual outcome scale. These approaches offer more direct insights than traditional Tobit models, validated by simulations and a real-world cohort study.

Keywords:
Tobit modelaverage-predicted-value approachcensoreddirect-marginalization approachoverall exposure effects

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Econometrics

Background:

  • The Tobit model, a censored regression model, is widely used but has limitations in estimating exposure effects on the original outcome scale.
  • Existing methods focus on latent variables, hindering direct interpretation of overall exposure effects.

Purpose of the Study:

  • To propose and evaluate novel methods for estimating direct exposure effects on the truncated dependent variable mean.
  • To compare a direct-marginalization approach with an average-predicted-value post-estimation approach.

Main Methods:

  • Developed a direct-marginalization approach using a reparameterized link function.
  • Utilized an alternative average-predicted-value, post-estimation method.
  • Conducted simulation studies to assess unbiasedness and robustness under various scenarios.

Main Results:

  • Both proposed methods provide unbiased and robust estimation of exposure effects.
  • Robustness may decrease with imbalanced covariates; model selection guidance is provided.
  • The methods were successfully applied to the GENOA cohort study.

Conclusions:

  • The direct-marginalization and average-predicted-value approaches enhance the estimation of exposure effects in censored regression models.
  • These methods offer improved interpretability for researchers across various fields.
  • Application to the GENOA study demonstrates practical utility in epidemiological research.