Impact of study design and statistical model in pharmacogenetic studies with gene‐treatment interaction

Abstract

Gene‐treatment interactions, just like drug‐drug interactions, can have dramatic effects on a patient response and therefore influence the clinician decision at the patient’s bedside. Crossover designs, although they are known to decrease the number of subjects in drug‐interaction studies, are seldom used in pharmacogenetic studies. We propose to evaluate, via realistic clinical trial simulations, to what extent crossover designs can help quantifying the gene‐treatment interaction effect. We explored different scenarios of crossover and parallel design studies comparing two symptom‐modifying treatments in a chronic and stable disease accounting for the impact of a one gene and one gene‐treatment interaction. We varied the number of subjects, the between and within subject variabilities, the gene polymorphism frequency and the effect sizes of the treatment, gene, and gene‐treatment interaction. Each simulated dataset was analyzed using three models: (i) estimating only the treatment effect, (ii) estimating the treatment and the gene effects, and (iii) estimating the treatment, the gene, and the gene‐treatment interaction effects. We showed how ignoring the gene‐treatment interaction results in the wrong treatment effect estimates. We also highlighted how crossover studies are more powerful to detect a treatment effect in the presence of a gene‐treatment interaction and more often lead to correct treatment attribution.