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Fitting marginalized two-part models to semicontinuous survey data arising from complex samples.

Valerie A Smith1,2,3, Brady T West4, Shiyu Zhang4

  • 1Center of Innovation to Accelerate Discovery and Practice Transformation, Durham VAMC, Durham, North Carolina, USA.

Health Services Research
|March 16, 2021
PubMed
Summary
This summary is machine-generated.

Accurate modeling of semicontinuous survey data requires accounting for complex sample designs. Ignoring these features can lead to incorrect inferences about healthcare spending and population characteristics.

Keywords:
complex sample survey datahealthcare expendituresmarginalized two-part models

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Area of Science:

  • Biostatistics
  • Health Economics
  • Survey Methodology

Background:

  • Semicontinuous data from complex surveys present unique modeling challenges.
  • Existing methods may not adequately account for intricate sample designs.
  • Accurate analysis is crucial for understanding healthcare expenditure patterns.

Purpose of the Study:

  • To extend marginalized two-part models for semicontinuous data within a design-based inferential framework.
  • To provide practical guidance on incorporating complex sample designs into statistical models.
  • To ensure accurate finite population inference from survey data.

Main Methods:

  • Utilized pseudo-Maximum Likelihood Estimation for parameter estimation.
  • Employed Jackknife Repeated Replication for sampling variance estimation.
  • Applied the approach to the 2014 Medical Expenditure Panel Survey (MEPS) data.

Main Results:

  • Increased family income (as % of poverty level) correlated with higher healthcare spending (5-6% increase per 100-point increase).
  • Individuals over 65 years old exhibited 4-5 times higher healthcare spending compared to younger individuals.
  • Accounting for complex sampling altered parameter estimates and widened confidence intervals compared to unweighted models.

Conclusions:

  • Complex sampling features must be considered when analyzing semicontinuous survey data.
  • Ignoring complex sampling can result in inaccurate finite population inference.
  • The proposed framework and methods enhance the reliability of survey data analysis.