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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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A marginalized two-part model for longitudinal semicontinuous data.

Valerie A Smith1,2, Brian Neelon3, John S Preisser2

  • 11 Center for Health Services Research in Primary Care, Durham Veterans Affairs Medical Center, USA.

Statistical Methods in Medical Research
|July 10, 2015
PubMed
Summary
This summary is machine-generated.

We introduce a new statistical model for analyzing health services research data, specifically semicontinuous longitudinal data. This model helps understand the average impact of factors like copayments on total healthcare spending for all individuals, not just users.

Keywords:
Semicontinuous datacopayment increasehealth care expenditureslog-skew-normal distributionmarginalized modelstwo-part models

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Semicontinuous data, common in health services research (e.g., healthcare expenditures), present analytical challenges.
  • Traditional two-part models for longitudinal semicontinuous data struggle to provide interpretable covariate effects for the entire population (users and non-users).

Purpose of the Study:

  • To propose a marginalized two-part model for longitudinal semicontinuous data.
  • To enable straightforward interpretation of covariate effects on the overall population mean.
  • To provide estimates of the population mean on the original scale and allow for dual population-average and subject-specific interpretations.

Main Methods:

  • Development of a marginalized two-part model for longitudinal data.
  • Utilizing a Bayesian estimation approach for flexibility in random-effect structures.
  • Application to evaluate the effect of copayment increases on healthcare expenditures.

Main Results:

  • The proposed model facilitates the estimation of covariate effects on the overall population mean.
  • It allows for the calculation of population means on the original, untransformed scale.
  • Demonstrates dual population-average and subject-specific interpretations for covariates.

Conclusions:

  • The marginalized two-part model offers a flexible and interpretable approach for analyzing longitudinal semicontinuous data in health services research.
  • This method enhances understanding of factors influencing overall healthcare utilization and expenditures.
  • The Bayesian framework supports complex modeling and estimation of population-level effects.