In older adults, the risk of injury with zolpidem exceeded that with alprazolam and lorazepam and was similar to that with diazepam. If the associations are causal, then the high incidence of these fractures implies that these treatment induce a substantial number of fractures and consequential costs. Further study of the association is imperative.
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 non-experimental 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 electrocardiographic 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 based on age, gender, 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, while the TOD was biased by the SNP main effect. For example, in the OPD, the false positive proportion (FPP) 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 FPP increased to 89% with SNP main effects >4 ms, although bias was reduced by 39% with adjustment for covariates affected by the SNP.
Conclusions
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.
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