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Case Definition and Design Sensitivity

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  • Dylan S. Small
  • Jing Cheng
  • M. Elizabeth Halloran
  • Paul R. Rosenbaum

Abstract

In a case-referent study, cases of disease are compared to noncases with respect to their antecedent exposure to a treatment in an effort to determine whether exposure causes some cases of the disease. Because exposure is not randomly assigned in the population, as it would be if the population were a vast randomized trial, exposed and unexposed subjects may differ prior to exposure with respect to covariates that may or may not have been measured. After controlling for measured preexposure differences, for instance by matching, a sensitivity analysis asks about the magnitude of bias from unmeasured covariates that would need to be present to alter the conclusions of a study that presumed matching for observed covariates removes all bias. The definition of a case of disease affects sensitivity to unmeasured bias. We explore this issue using: (i) an asymptotic tool, the design sensitivity, (ii) a simulation for finite samples, and (iii) an example. Under favorable circumstances, a narrower case definition can yield an increase in the design sensitivity, and hence an increase in the power of a sensitivity analysis. Also, we discuss an adaptive method that seeks to discover the best case definition from the data at hand while controlling for multiple testing. An implementation in R is available as SensitivityCaseControl .

Suggested Citation

  • Dylan S. Small & Jing Cheng & M. Elizabeth Halloran & Paul R. Rosenbaum, 2013. "Case Definition and Design Sensitivity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1457-1468, December.
    • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:504:p:1457-1468
    DOI: 10.1080/01621459.2013.820660
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    Cited by:

    1. Siyu Heng & Hyunseung Kang & Dylan S. Small & Colin B. Fogarty, 2021. "Increasing power for observational studies of aberrant response: An adaptive approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 482-504, July.
    2. Paul R. Rosenbaum & Dylan S. Small, 2017. "An adaptive Mantel–Haenszel test for sensitivity analysis in observational studies," Biometrics, The International Biometric Society, vol. 73(2), pages 422-430, June.
    3. Paul R. Rosenbaum, 2015. "Bahadur Efficiency of Sensitivity Analyses in Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 205-217, March.

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