Hypothesis Tests for Neyman's Bias in Case–Control Studies

ABSTRACT

Survival bias is a long recognized problem in case–control studies, and many varieties of bias can come under this umbrella term. We focus on one of them, termed Neyman's bias or ‘prevalence–incidence bias’. It occurs in case–control studies when exposure affects both disease and disease-induced mortality, and we give a formula for the observed, biased odds ratio under such conditions. We compare our result with previous investigations into this phenomenon and consider models under which this bias may or may not be important. Finally, we propose three hypothesis tests to identify when Neyman's bias may be present in case–control studies. We apply these tests to three data sets, one of stroke mortality, another of brain tumors, and the last of atrial fibrillation, and find some evidence of Neyman's bias in the former two cases, but not the last case.

KEYWORDS:

• Odds ratio
• survival bias
• truncation

Acknowledgments

The authors wish to thank Dr. Deborah Blacker for many helpful comments used in the preparation of this manuscript as well as Drs. Gregory Cairncross and David Louis for use of the brain tumor data. Dr. Guido Falcone provided invaluable support in the preparation of the ischemic stroke data set. The MGH ischemic stroke data set was supported by the American Heart Association/Bugher Foundation Centers for Stroke Prevention Research, the National Institute of Neurological Disorders and Stroke, the Deane Institute for Integrative Study of Atrial Fibrillation and Stroke, and the Keane Stroke Genetics Research Fund.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Dr. Anderson is supported by a Clinical Research Training Fellowship from the American Brain Foundation. Prof. Rebecca Betensky is supported by the National Institutes of Health grant CA075971. Dr. Swanson was supported by the National Institutes of Health Training CA075971,T32 NS048005 while the work was completed at Harvard School of Public Health.