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Spatial small area smoothing models for handling survey data with nonresponse.

K Watjou1, C Faes1, A Lawson2

  • 1Interuniversity Institute for Statistics and Statistical Bioinformatics, Hasselt University, 3590, Hasselt, Belgium.

Statistics in Medicine
|July 4, 2017
PubMed
Summary
This summary is machine-generated.

Weight adjustment effectively reduces bias in spatial smoothing models for small area estimation, particularly in health surveys with missing data. This method improves the reliability of spatial trend estimates when dealing with nonresponse.

Keywords:
complex survey designdisease mappinghierarchical Bayesian modellingintegrated nested Laplace approximationmissing data

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Area of Science:

  • Statistics
  • Spatial Analysis
  • Small Area Estimation

Background:

  • Spatial smoothing models are crucial for small area estimation, but missing data (nonresponse) can introduce bias.
  • Design weights are essential in complex survey designs for accurate estimation.
  • Spatial trend estimation is challenged by missing data, impacting reliability.

Purpose of the Study:

  • To investigate the impact of nonresponse on spatial models in health surveys.
  • To evaluate different missingness mechanisms and degrees of missingness.
  • To assess the effectiveness of weight adjustment for bias correction.

Main Methods:

  • Utilized spatial smoothing models with binary responses and design weights.
  • Conducted an extensive simulation study to analyze nonresponse impacts.
  • Employed R, INLA, and other packages for computational analysis.

Main Results:

  • Weight adjustment demonstrated a beneficial effect on bias in the missing at random (MAR) setting.
  • The study confirmed the impact of missingness mechanisms and degrees on spatial model estimates.
  • Estimated the geographical distribution of perceived health using Belgian Health Interview Survey data.

Conclusions:

  • Weight adjustment is a valuable technique for mitigating bias caused by nonresponse in spatial health surveys.
  • Findings support the use of adjusted weights for more reliable spatial trend estimation.
  • The study provides practical insights for analyzing health survey data with missing values.