CitationAvery, Christy L.; Der, Jane S.; Whitsel, Eric A.; & Stürmer, Til (2014). Comparison of Study Designs Used to Detect and Characterize Pharmacogenomic Interactions in Nonexperimental Studies: A Simulation Study. Pharmacogenetics and Genomics, 24(3), 146-155. PMCID: PMC3946643
AbstractOBJECTIVES: 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.
Reference TypeJournal Article
Journal TitlePharmacogenetics and Genomics
Author(s)Avery, Christy L.
Der, Jane S.
Whitsel, Eric A.