OBJECTIVES: Adverse drug reactions are common, serious, difficult to predict, and may be influenced by genetics, prompting the increasing popularity of pharmacogenomic studies. Many pharmacogenomic studies are conducted in nonexperimental settings, yet little is known about the influence of confounding by contraindication. We, therefore, compared the two designs [the overall population (OPD) and the treated-only (TOD) design] by simulating a pharmacogenomic study of the ECG QT interval (QT). METHODS: Simulations were informed by data from the Atherosclerosis Risk in Communities Study and a literature review examining QT, QT-prolonging drug use, and modification by single nucleotide polymorphisms (SNP). Drug treatment was assigned on the basis of age, sex, and QTlong, representing confounding by contraindication. QT was simulated as a function of drug treatment, one SNP, the drug-SNP interaction, and clinical covariates. RESULTS: Failure to adjust for confounding by contraindication produced a varying degree of bias in the OPD, whereas the TOD was biased by the SNP main effect. For example, in the OPD, the false-positive proportion for the drug-SNP interaction was 5% across the range of SNP main effects (0-10 ms), but increased to 19% without adjusting for confounding by contraindication. In the TOD, the false-positive proportion increased to 89% with SNP main effects greater than 4 ms, although bias was reduced by 39% with adjustment for covariates affected by the SNP. CONCLUSION: The potential for bias from confounding by contraindication (OPD) should be weighed against bias from SNP main effects (TOD) when selecting the study design that best suits the given context.
OBJECTIVES: Adverse drug reactions are common, serious, difficult to predict, and may be influenced by genetics, prompting the increasing popularity of pharmacogenomic studies. Many pharmacogenomic studies are conducted in nonexperimental settings, yet little is known about the influence of confounding by contraindication. We, therefore, compared the two designs [the overall population (OPD) and the treated-only (TOD) design] by simulating a pharmacogenomic study of the ECG QT interval (QT). METHODS: Simulations were informed by data from the Atherosclerosis Risk in Communities Study and a literature review examining QT, QT-prolonging drug use, and modification by single nucleotide polymorphisms (SNP). Drug treatment was assigned on the basis of age, sex, and QTlong, representing confounding by contraindication. QT was simulated as a function of drug treatment, one SNP, the drug-SNP interaction, and clinical covariates. RESULTS: Failure to adjust for confounding by contraindication produced a varying degree of bias in the OPD, whereas the TOD was biased by the SNP main effect. For example, in the OPD, the false-positive proportion for the drug-SNP interaction was 5% across the range of SNP main effects (0-10 ms), but increased to 19% without adjusting for confounding by contraindication. In the TOD, the false-positive proportion increased to 89% with SNP main effects greater than 4 ms, although bias was reduced by 39% with adjustment for covariates affected by the SNP. CONCLUSION: The potential for bias from confounding by contraindication (OPD) should be weighed against bias from SNP main effects (TOD) when selecting the study design that best suits the given context.
Authors: Ihab Hajjar; Stephen Kritchevsky; Anne B Newman; Rongling Li; Kristine Yaffe; Eleanor M Simonsick; Lewis A Lipsitz Journal: J Am Geriatr Soc Date: 2010-06 Impact factor: 5.562
Authors: S Volpi; C Heaton; K Mack; J B Hamilton; R Lannan; C D Wolfgang; L Licamele; M H Polymeropoulos; C Lavedan Journal: Mol Psychiatry Date: 2008-06-03 Impact factor: 15.992
Authors: C L Avery; C M Sitlani; D E Arking; D K Arnett; J C Bis; E Boerwinkle; B M Buckley; Y-D Ida Chen; A J M de Craen; M Eijgelsheim; D Enquobahrie; D S Evans; I Ford; M E Garcia; V Gudnason; T B Harris; S R Heckbert; H Hochner; A Hofman; W-C Hsueh; A Isaacs; J W Jukema; P Knekt; J A Kors; B P Krijthe; K Kristiansson; M Laaksonen; Y Liu; X Li; P W Macfarlane; C Newton-Cheh; M S Nieminen; B A Oostra; G M Peloso; K Porthan; K Rice; F F Rivadeneira; J I Rotter; V Salomaa; N Sattar; D S Siscovick; P E Slagboom; A V Smith; N Sotoodehnia; D J Stott; B H Stricker; T Stürmer; S Trompet; A G Uitterlinden; C van Duijn; R G J Westendorp; J C Witteman; E A Whitsel; B M Psaty Journal: Pharmacogenomics J Date: 2013-03-05 Impact factor: 3.550
Authors: Raymond Noordam; Colleen M Sitlani; Christy L Avery; James D Stewart; Stephanie M Gogarten; Kerri L Wiggins; Stella Trompet; Helen R Warren; Fangui Sun; Daniel S Evans; Xiaohui Li; Jin Li; Albert V Smith; Joshua C Bis; Jennifer A Brody; Evan L Busch; Mark J Caulfield; Yii-Der I Chen; Steven R Cummings; L Adrienne Cupples; Qing Duan; Oscar H Franco; Rául Méndez-Giráldez; Tamara B Harris; Susan R Heckbert; Diana van Heemst; Albert Hofman; James S Floyd; Jan A Kors; Lenore J Launer; Yun Li; Ruifang Li-Gao; Leslie A Lange; Henry J Lin; Renée de Mutsert; Melanie D Napier; Christopher Newton-Cheh; Neil Poulter; Alexander P Reiner; Kenneth M Rice; Jeffrey Roach; Carlos J Rodriguez; Frits R Rosendaal; Naveed Sattar; Peter Sever; Amanda A Seyerle; P Eline Slagboom; Elsayed Z Soliman; Nona Sotoodehnia; David J Stott; Til Stürmer; Kent D Taylor; Timothy A Thornton; André G Uitterlinden; Kirk C Wilhelmsen; James G Wilson; Vilmundur Gudnason; J Wouter Jukema; Cathy C Laurie; Yongmei Liu; Dennis O Mook-Kanamori; Patricia B Munroe; Jerome I Rotter; Ramachandran S Vasan; Bruce M Psaty; Bruno H Stricker; Eric A Whitsel Journal: J Med Genet Date: 2016-12-30 Impact factor: 6.318