{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["10(4)"],"submitter":["Couffignal C"],"pubmed_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."],"journal":["CPT: pharmacometrics & systems pharmacology"],"pagination":["340-349"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8099447"],"repository":["biostudies-literature"],"pubmed_title":["Impact of study design and statistical model in pharmacogenetic studies with gene-treatment interaction."],"pmcid":["PMC8099447"],"pubmed_authors":["Couffignal C","Mentre F","Bertrand J"],"additional_accession":[]},"is_claimable":false,"name":"Impact of study design and statistical model in pharmacogenetic studies with gene-treatment interaction.","description":"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.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Apr","modification":"2024-11-09T05:23:58.878Z","creation":"2022-02-10T09:56:28.869Z"},"accession":"S-EPMC8099447","cross_references":{"pubmed":["33951752"],"doi":["10.1002/psp4.12624"]}}