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Published on: September 16, 2022
Impact of Selection Bias on Estimation of Subsequent Event Risk
Yi-Juan Hu1, Amand F Schmidt2, Frank Dudbridge2
1From the Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA (Y.-J.H., Z.L., P.L.); Groningen Research Institute of Pharmacy, University of Groningen, the Netherlands (A.F.S.); Institute of Cardiovascular Science and The Farr Institute, University College London, United Kingdom (A.F.S., A.D.H., F.W.A., R.P.); Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, United Kingdom (F.D.); Department of Health Sciences, University of Leicester, United Kingdom (F.D.); Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, United Kingdom (M.V.H.); Medical Research Council Population Health Research Unit at the University of Oxford, United Kingdom (M.V.H.); Department of Medicine, McGill University, Montreal Quebec, Canada (J.M.B.); Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, The Netherlands (V.T., F.W.A.); Division of Cardiology, Department of Medicine, Emory Clinical Cardiovascular Research Institute, Emory University School of Medicine, Atlanta, GA (A.A.Q.); Intermountain Heart Institute, Intermountain Medical Center, Murray, UT (R.O.M., B.D.H.); Department of Biomedical Informatics, University of Utah, Salt Lake City (B.D.H.); and Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia (Q.L.). yijuan.hu@emory.edu amand.schmidt@ucl.ac.uk.
Background:
Studies of recurrent or subsequent disease events may be susceptible to bias caused by selection of subjects who both experience and survive the primary indexing event. Currently, the magnitude of any selection bias, particularly for subsequent time-to-event analysis in genetic association studies, is unknown.
Methods And Results:
We used empirically inspired simulation studies to explore the impact of selection bias on the marginal hazard ratio for risk of subsequent events among those with established coronary heart disease. The extent of selection bias was determined by the magnitudes of genetic and nongenetic effects on the indexing (first) coronary heart disease event. Unless the genetic hazard ratio was unrealistically large (>1.6 per allele) and assuming the sum of all nongenetic hazard ratios was <10, bias was usually <10% (downward toward the null). Despite the low bias, the probability that a confidence interval included the true effect decreased (undercoverage) with increasing sample size because of increasing precision. Importantly, false-positive rates were not affected by selection bias.
Conclusions:
In most empirical settings, selection bias is expected to have a limited impact on genetic effect estimates of subsequent event risk. Nevertheless, because of undercoverage increasing with sample size, most confidence intervals will be over precise (not wide enough). When there is no effect modification by history of coronary heart disease, the false-positive rates of association tests will be close to nominal.
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