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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Published on: January 8, 2020

Pattern-mixture models for analyzing normal outcome data with proxy respondents.

Michelle Shardell1, Gregory E Hicks, Ram R Miller

  • 1Department of Epidemiology and Preventive Medicine, University of Maryland, Baltimore, MD, U.S.A. mshardel@epi.umaryland.edu

Statistics in Medicine
|June 11, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces pattern-mixture models to improve data analysis when using proxy respondents for older adults, reducing measurement error and bias in research findings.

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Last Updated: Jun 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Gerontology
  • Biostatistics
  • Epidemiology

Background:

  • Studies involving older adults frequently use subjective measures.
  • Proxies often provide data when subjects cannot respond, leading to incomplete datasets.
  • Current methods of substituting proxy data can introduce bias and measurement error.

Purpose of the Study:

  • To propose pattern-mixture models for analyzing data with proxy respondents.
  • To address limitations of current methods for handling missing data in older adult studies.
  • To improve the accuracy of parameter estimates in research involving proxy data.

Main Methods:

  • Utilizing pattern-mixture models to link non-identifiable and identifiable parameters.
  • Implementing three interpretable pattern-mixture restrictions for proxy data.
  • Employing maximum likelihood and multiple imputation for estimation.

Main Results:

  • The proposed pattern-mixture models offer a robust approach to analyzing proxy data.
  • This method can mitigate measurement error and parameter bias.
  • Demonstrated application in a cohort of elderly hip-fracture patients.

Conclusions:

  • Pattern-mixture models provide a statistically sound framework for incorporating proxy data.
  • This approach enhances the reliability of findings in studies of older adults.
  • The methods are valuable for research involving subjective constructs and proxy reporting.