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Regression analysis of multiple source and multiple informant data from complex survey samples.

Nicholas J Horton1, Garrett M Fitzmaurice

  • 1Department of Mathematics, Smith College, College Lane, Northampton, MA 01063, USA. nhorton@smith.edu

Statistics in Medicine
|September 3, 2004
PubMed
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This tutorial presents regression methods for analyzing complex survey data from multiple sources, such as various informants or instruments. These techniques accommodate survey complexities like stratification, clustering, and sampling weights for robust analysis.

Area of Science:

  • Statistics
  • Survey Methodology
  • Biostatistics

Background:

  • Analyzing data from multiple sources (e.g., different informants or instruments) is common in surveys.
  • Complex sample survey designs involve stratification, clustering, and sampling weights, complicating data analysis.
  • Existing regression methods may not adequately handle the correlated nature of multiple-source data within complex surveys.

Purpose of the Study:

  • To describe regression-based methods for analyzing multiple-source data from complex sample surveys.
  • To demonstrate how these methods can be extended to incorporate survey design features.
  • To provide practical guidance on fitting these models using statistical software.

Main Methods:

  • Regression models are presented as extensions of generalized linear models for correlated outcomes.

Related Experiment Videos

  • Methods are adapted to account for stratification, clustering, and sampling weights in survey data.
  • The use of general-purpose statistical software for fitting these models is discussed.

    • Main Results:

    • Regression models can effectively analyze multiple-source risk factors and outcomes.
    • The proposed methods successfully integrate complex survey design features.
    • The tutorial illustrates the application of these methods with real-world study data.

    Conclusions:

    • Regression-based approaches offer a flexible framework for analyzing multiple-source data in complex surveys.
    • These methods enhance the ability to derive valid inferences from multi-informant or multi-method survey data.
    • The described techniques are applicable across various research fields utilizing complex survey designs.