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A corrected formulation for marginal inference derived from two-part mixed models for longitudinal semi-continuous

Brian Dm Tom1, Li Su2, Vernon T Farewell2

  • 1Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, UK brian.tom@mrc-bsu.cam.ac.uk.

Statistical Methods in Medical Research
|November 9, 2013
PubMed
Summary
This summary is machine-generated.

This study corrects a previous formulation for estimating population-averaged effects in two-part mixed models for semi-continuous data. It also explores using these models for overall marginal mean inferences, offering practical relevance.

Keywords:
bridge distributionexcess zeroslongitudinal datarandom effects

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Semi-continuous data, common in health and economics, present unique modeling challenges due to the mixture of zero and positive continuous values.
  • Two-part mixed models offer a flexible framework for analyzing such data but deriving population-averaged (marginal) effects can be complex.
  • Existing methods for estimating marginal effects in the continuous component of two-part models have shown formulation inaccuracies.

Purpose of the Study:

  • To present a corrected formulation for estimating marginal effects in the continuous part of two-part mixed models.
  • To explore the application of two-part mixed models for inferences on the overall marginal mean.
  • To provide a more accurate and practically relevant approach for analyzing semi-continuous data.

Main Methods:

  • Utilized a corrected formulation for the continuous component of two-part mixed models.
  • Investigated methods for estimating population-averaged (marginal) effects.
  • Explored the estimation of the overall marginal mean for semi-continuous data.

Main Results:

  • A corrected formulation for marginal effects in the continuous part of two-part mixed models was derived.
  • The study demonstrated the utility of two-part models for overall marginal mean inference.
  • The proposed methods offer improved accuracy and practical applicability for semi-continuous data analysis.

Conclusions:

  • The corrected formulation enhances the accurate estimation of marginal effects for semi-continuous data.
  • Inferences on the overall marginal mean using two-part models are feasible and practically valuable.
  • This work provides a more robust statistical framework for analyzing data with a mixture of zeros and continuous positive values.