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Analysis of Large Health Surveys: Accounting for the Sampling Design

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  • Edward L. Korn
  • Barry I. Graubard

Abstract

Large scale health surveys offer an opportunity to study associations between risk factors and outcomes in a population‐based setting. Their complicated multistage sampling designs with differential probabilities of sampling individuals can make their analysis unstraightforward. Classical ‘design‐based’ methods that yield approximately unbiased estimators of associations and standard errors can be highly inefficient. Model‐based methods require assumptions which, if wrong, can lead to biased estimators of associations and standard errors. This paper examines the implications of utilizing the sample clustering and sample weights in the analysis of survey data. The approach is to estimate the inefficiency of using these aspects of the sampling design in a design‐based analysis when actually it was unnecessary to do so. If the inefficiency is small, then that aspect of the design is used in a design‐based fashion. Otherwise, additional modelling assumptions are incorporated into the analysis. By focusing attention on risk factor–outcome associations in large health surveys, specific recommendations for practitioners are given. The issues are demonstrated with real survey data including two controversial analyses previously published in medical references.

Suggested Citation

  • Edward L. Korn & Barry I. Graubard, 1995. "Analysis of Large Health Surveys: Accounting for the Sampling Design," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(2), pages 263-295, March.
    • Handle: RePEc:bla:jorssa:v:158:y:1995:i:2:p:263-295
    DOI: 10.2307/2983292
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    Citations

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    Cited by:

    1. Archer, Kellie J. & Lemeshow, Stanley & Hosmer, David W., 2007. "Goodness-of-fit tests for logistic regression models when data are collected using a complex sampling design," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4450-4464, May.
    2. Robert Kaestner & Elizabeth Tarlov, 2006. "Changes in the welfare caseload and the health of low-educated mothers," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 25(3), pages 623-643.
    3. Miriam Kesselmeier & Norbert Benda & André Scherag, 2020. "Effect size estimates from umbrella designs: Handling patients with a positive test result for multiple biomarkers using random or pragmatic subtrial allocation," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-24, August.
    4. Michele M. Wood & Dennis S. Mileti & Megumi Kano & Melissa M. Kelley & Rotrease Regan & Linda B. Bourque, 2012. "Communicating Actionable Risk for Terrorism and Other Hazards⋆," Risk Analysis, John Wiley & Sons, vol. 32(4), pages 601-615, April.
    5. Barry I. Graubard & Edward L. Korn, 1999. "Predictive Margins with Survey Data," Biometrics, The International Biometric Society, vol. 55(2), pages 652-659, June.
    6. D.S. Jang & J.L. Eltinge, "undated". "Evaluation of Descriptive Analyses of Survey Variances and Confidence Interval Widths," Mathematica Policy Research Reports d39db346f92e4012a19f20487, Mathematica Policy Research.
    7. Stanislav Kolenikov, 2010. "Resampling variance estimation for complex survey data," Stata Journal, StataCorp LP, vol. 10(2), pages 165-199, June.
    8. Nianbo Dong & Elizabeth A. Stuart & David Lenis & Trang Quynh Nguyen, 2020. "Using Propensity Score Analysis of Survey Data to Estimate Population Average Treatment Effects: A Case Study Comparing Different Methods," Evaluation Review, , vol. 44(1), pages 84-108, February.
    9. Verónica Herrero & Mónica Bocco, 2007. "Comparación de Ponderaciones en Regresiones Probit Simultáneas en un Modelo para la Estimación de la Participación Laboral," Revista de Economía y Estadística, Universidad Nacional de Córdoba, Facultad de Ciencias Económicas, Instituto de Economía y Finanzas, vol. 45(2), pages 95-124, Diciembre.
    10. repec:mpr:mprres:3373 is not listed on IDEAS

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