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Impact of Selection Bias on Estimation of Subsequent Event Risk

2017; Lippincott Williams & Wilkins; Volume: 10; Issue: 5 Linguagem: Inglês

10.1161/circgenetics.116.001616

1942-325X

Yi‐Juan Hu, Amand F. Schmidt, Frank Dudbridge, Michael V. Holmes, James M. Brophy, Vinicius Tragante, Ziyi Li, Peizhou Liao, Arshed A. Quyyumi, Raymond O. McCubrey, Benjamin D. Horne, Aroon D. Hingorani, Folkert W. Asselbergs, Riyaz Patel, Long Qi, Axel Åkerblom, Ale Algra, Hooman Allayee, Peter Almgren, Jeffrey L. Anderson, Maria Grazia Andreassi, Chiara Viviani Anselmi, Diego Ardissino, Benoît J. Arsenault, Christie M. Ballantyne, E.V. Baranova, Hassan Behloui, Thomas O. Bergmeijer, Connie R. Bezzina, Eyþór Björnsson, Simon C. Body, Bram Boeckx, Eric Boersma, Eric Boerwinkle, Peter Bogaty, Peter S. Braund, Lutz Philipp Breitling, Hermann Brenner, Carlo Briguori, Jasper J. Brugts, Ralph Burkhardt, Vicky A. Cameron, John F. Carlquist, Clara Carpeggiani, Kathryn F. Carruthers, Gavino Casu, Gianluigi Condorelli, Sharon Cresci, Nicolas Danchin, Ulf dé Fairé, John Deanfield, Graciela Delgado, Panos Deloukas, Kenan Direk, Robert N. Doughty, Heinz Drexel, Núbia E. Duarte, Marie‐Pierre Dubé, Line Dufresne, James C. Engert, Niclas Eriksson, Natalie Fitzpatrick, Luisa Foco, Ian Ford, Keith A.A. Fox, Bruna Gigante, Crystel M. Gijsberts, Domenico Girelli, Yan Gong, Daníel F. Guðbjartsson, Emil Hagström, Jaana Hartiala, Stanley L. Hazen, Claes Held, Anna Helgadóttir, Harry Hemingway, Mahyar Heydarpour, Imo E. Hoefer, Kees Hovingh, Jaroslav A. Hubáček, Stefan James, Julie A. Johnson, J. Wouter Jukema, Marcin Kaczor, Karol Kamiński, Jiří Kettner, Marek Kiliszek, Marcus E. Kleber, Olaf H. Klungel, Daniel Kofink, Mika Kohonen, Salma Kotti, Pekka Kuukasjärvi, Bo Lagerqvist, Diether Lambrechts, Chim C. Lang, Jari Laurikka, Karin Leander, Vei‐Vei Lee, Terho Lehtimäki, Andreas Leiherer, Petra A. Lenzini, Daniel L. Levin, Daniel Lindholm, Marja‐Liisa Lokki, Paulo A. Lotufo, Leo‐Pekka Lyytikäinen, Bakhtawar K. Mahmoodi, Anke H. Maitland‐van der Zee, Nicola Martinelli, Winfried März, Nicola Marziliano, Ruth McPherson, Olle Melander, Ute Mons, Jochen D. Muehlschlegel, Joseph B. Muhlestein, Cristopher P. Nelson, Chris Newton Cheh, Oliviero Olivieri, Grzegorz Opolski, Colin N. A. Palmer, Guillaume Paré, Gerard Pasterkamp, Carl J. Pepine, Witold Pepiński, Alexandre C. Pereira, Anna P. Pilbrow, Louise Pilote, Jan Piťha, Rafał Płoski, Mark Richards, Christoph H. Saely, Nilesh J. Samani, Ayman Samman‐Tahhan, Marek Sanak, Pratik B. Sandesara, Naveed Sattar, Markus Scholz, Agneta Siegbahn, Tabassome Simon, Juha Sinisalo, J. G. Smith, John A. Spertus, Kāri Stefánsson, Alexandre F.R. Stewart, David J. Stott, Wojciech Szczeklik, Anna Szpakowicz, Michael W.T. Tanck, W.H. Wilson Tang, Jean‐Claude Tardif, J M Ten Berg, Andrej Teren, George Thanassoulis, Joachim Thiery, Guðmundur Þorgeirsson, Guðmar Þorleifsson, Unnur Þorsteinsdóttir, Adam Timmis, Stella Trompet, Frans Van de Werf, Yolanda van der Graaf, Pim van der Haarst, Sander W. van der Laan, Ragnar O. Vilmundarson, Salim S. Virani, Frank L.J. Visseren, Efthymia Vlachopoulou, Lars Wallentin, Johannes Waltenberger, Els Wauters, Arthur A.M. Wilde,

Cardiovascular Function and Risk Factors

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.