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Calculating Sample Sizes in the Presence of Confounding Variables

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  • S. R. Wilson
  • I. Gordon

Abstract

A major issue in the design of many studies is to determine the “required” number of observations. Much of the statistical literature on sample size estimation, particularly for medical studies, is devoted to considering a very simple design, involving the testing of the difference between proportions in two groups. Accommodation of potential confounding variables at the design stage is more difficult. This paper outlines a general theory based on generalized linear models for accommodating such variables. The theory is applied to three examples from medical research.

Suggested Citation

  • S. R. Wilson & I. Gordon, 1986. "Calculating Sample Sizes in the Presence of Confounding Variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 35(2), pages 207-213, June.
    • Handle: RePEc:bla:jorssc:v:35:y:1986:i:2:p:207-213
    DOI: 10.2307/2347271
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    Cited by:

    1. Jason Dietrich, 2005. "The effects of sampling strategies on the small sample properties of the logit estimator," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(6), pages 543-554.

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