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Sensitivity Analysis and Bias Analysis

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Handbook of Epidemiology

Abstract

Over recent decades recognition has grown that the conventional statistical models used to analyze epidemiological data cannot be reasonably claimed to be correct in the way most textbooks treat them to be. In particular, conventional models for epidemiological data-generating processes cannot be credibly taken to represent targets of primary scientific interest. For example, a logistic model for the regression of an observed disease indicator on covariate measurements would only rarely correspond closely to the causal effects on disease of the risk factors represented by the measurements. The discrepancies between the statistical model parameters and the underlying target effects are often called systematic errors, biases, or bias sources. Large biases undermine the interpretation of both frequentist statistics (such as confidence intervals) and Bayesian statistics (such as posterior intervals).

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Author information

Authors and Affiliations

  1. Department of Epidemiology, School of Public Health, University of California, 90095-1772, Los Angeles, CA, USA

    Sander Greenland

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  1. Sander Greenland
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Editor information

Editors and Affiliations

  1. Department of Epidemiological Methods and Etiologic Research, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany

    Wolfgang Ahrens

  2. Department of Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany

    Iris Pigeot

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Greenland, S. (2014). Sensitivity Analysis and Bias Analysis. In: Ahrens, W., Pigeot, I. (eds) Handbook of Epidemiology. Springer, New York, NY. https://doi.org/10.1007/978-0-387-09834-0_60

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  • DOI: https://doi.org/10.1007/978-0-387-09834-0_60

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-09833-3

  • Online ISBN: 978-0-387-09834-0

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