Comparison of study designs used to detect and characterize pharmacogenomic interactions in nonexperimental studies

Abstract

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.

A genome-wide interaction analysis of tricyclic/tetracyclic antidepressants and RR and QT intervals: a pharmacogenomics study from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium

“…Fifth, we were not able to adjust for potential confounders of SNP-TCA interactions. However, confounders with strong effects on the drug-outcome association were shown to only modestly bias the results 44. And last, TCA doses may have been titrated to provide optimal blood concentrations, according to participant CYP2D6 and CYP2C19 genotypes.…”

The rs13064411 polymorphism in the WDR52 gene, associated with PCSK9 levels, modifies statin-induced changes in serum total and LDL cholesterol levels

“…We used random-effect repeated measurements to investigate whether there was a statistically significant interaction between the rs13064411 polymorphism and current use of statin therapy on serum cholesterol measurements [37]. Past users were included in the group of nonusers.…”

A critical appraisal of pharmacogenetic inference

“…The treated‐only design essentially tries to limit the issue of confounding by contraindication whilst improving statistical efficiency . As the name suggests, this design limits the analysis to those exposed to the drug, thereby leaving out the subjects who might have had a pertinent contraindication to treatment.…”

“…A clear drawback are that the main effects of genetic variants on the outcome are inseparable from drug‐treatment interaction effects. Observed loci may thus be associated with the natural course of the disease . In these cases, leveraging publically available data from genome‐wide association studies (GWAS) may help to substantiate the claim of absence of a main effect of a genetic variant on the outcome of interest.…”

“…35,36 The treated-only design essentially tries to limit the issue of confounding by contraindication whilst improving statistical efficiency. 37 As the name suggests, this design limits the analysis to those exposed to the drug, thereby leaving out the subjects who might have had a pertinent contraindication to treatment. This contrasts with cohorts which do include an untreated control group, in which confounding by (contra)indication is more commonly addressed through statistical adjustment, although applying stricter inclusion criteria at enrolment may also limit this issue.…”