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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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Combining forward and backward mortality estimation.

Dan A Black1,2,3, Yu-Chieh Hsu1,2, Seth G Sanders2,4

  • 1a University of Chicago.

Population Studies
|June 15, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal method for combining forward and backward projection techniques to estimate death probabilities from incomplete demographic data. The generalized method of moments framework improves accuracy for small subpopulations.

Keywords:
Mortalitybackward estimationforward estimation

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Area of Science:

  • Demography
  • Statistical Modeling
  • Population Studies

Background:

  • Estimating demographic parameters, such as death probabilities, is challenging when data sources are incomplete.
  • Traditional methods like back-projection and forward/inverse projection have limitations in handling incomplete data.

Purpose of the Study:

  • To develop an optimal method for combining forward and backward projection approaches for demographic estimation.
  • To apply this generalized method of moments (GMM) framework to estimate death probabilities for small U.S. subpopulations.

Main Methods:

  • The study utilizes a generalized method of moments (GMM) framework.
  • This GMM approach optimally combines forward and backward projection techniques.
  • Data from vital statistics records and census samples are integrated.

Main Results:

  • The developed method effectively estimates death probabilities for small subpopulations.
  • Application to U.S. men born 1930-1939 by state, cohort, and race demonstrates the method's utility.
  • The GMM framework provides a robust approach to data integration.

Conclusions:

  • Combining forward and backward projection methods offers an optimal solution for demographic estimation with incomplete data.
  • The GMM framework is a powerful tool for improving the accuracy of death probability estimates.
  • This research provides valuable insights for demographic analysis of small populations.