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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Conditional pseudolikelihood methods for clustered ordinal, multinomial, or count outcomes with complex survey data.

Babette A Brumback1, Zhuangyu Cai, Zhulin He

  • 1Department of Biostatistics, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL 32611, USA. brumback@ufl.edu

Statistics in Medicine
|September 15, 2012
PubMed
Summary
This summary is machine-generated.

New statistical methods address confounding in complex survey data for clustered outcomes. These conditional pseudolikelihood approaches improve analysis of neighborhood effects on health behaviors.

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Confounding by unmeasured neighborhood characteristics poses challenges in analyzing complex survey data.
  • Existing methods for clustered ordinal outcomes require sampling design joint probabilities for within-neighborhood pairs.

Purpose of the Study:

  • To extend conditional pseudolikelihood methods for clustered ordinal outcomes.
  • To develop methodologies for clustered multinomial and count outcomes using baseline category logit and loglinear models, respectively.
  • To adjust for neighborhood confounding in complex survey data analysis.

Main Methods:

  • Development of conditional pseudolikelihood methodology for clustered multinomial and count outcomes.
  • Utilizing sampling design joint probabilities for within-neighborhood pairs.
  • Demonstration of theoretical validity and empirical support through simulations.
  • Application of methods using standard logistic regression software for complex survey data (e.g., SAS PROC SURVEYLOGISTIC).

Main Results:

  • The proposed methods provide valid estimators and asymptotic sampling distributions.
  • Simulations confirm the theoretical validity of the developed statistical approaches.
  • Application to the 2008 Florida Behavioral Risk Factor Surveillance System survey revealed disparities in dental cleaning frequency.

Conclusions:

  • The new methodologies effectively adjust for neighborhood confounding in clustered multinomial and count outcome data.
  • These methods are computationally feasible using existing complex survey data software.
  • The study highlights disparities in dental cleaning frequency, adjusted for neighborhood effects.