ArticlePharmacogenomics2016
Evaluation of methodology for the analysis of 'time-to-event' data in pharmacogenomic genome-wide association studies.
Article in Pharmacogenomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Genetic association studies using disease liabilities from deep neural networks.American journal of human genetics · 2025Article
- Analysis of follow-up data in large biobank cohorts: a review of methodology.Frontiers in genetics · 2025Article
- Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss Functions.Statistics in medicine · 2024Article
- Genetic association studies using disease liabilities from deep neural networks.medRxiv : the preprint server for health sciences · 2024Article
- ADuLT: An efficient and robust time-to-event GWAS.Nature communications · 2023Article
- Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis.Nature communications · 2021Article
- Suicide Among Persons With Dementia, Georgia, 2013 to 2016.Journal of geriatric psychiatry and neurology · 2019Article
- SurvivalGWAS_SV: software for the analysis of genome-wide association studies of imputed genotypes with "time-to-event" outcomes.BMC bioinformatics · 2017Article
- SurvivalGWAS_Power: a user friendly tool for power calculations in pharmacogenetic studies with "time to event" outcomes.BMC bioinformatics · 2016Article
- genipe: an automated genome-wide imputation pipeline with automatic reporting and statistical tools.Bioinformatics (Oxford, England) · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
aimTo evaluate the power to detect associations between SNPs and time-to-event outcomes across a range of pharmacogenomic study designs while comparing alternative regression approaches. MATERIALS &
methodsSimulations were conducted to compare Cox proportional hazards modeling accounting for censoring and logistic regression modeling of a dichotomized outcome at the end of the study.
resultsThe Cox proportional hazards model was demonstrated to be more powerful than the logistic regression analysis. The difference in power between the approaches was highly dependent on the rate of censoring.
conclusionInitial evaluation of single-nucleotide polymorphism association signals using computationally efficient software with dichotomized outcomes provides an effective screening tool for some design scenarios, and thus has important implications for the development of analytical protocols in pharmacogenomic studies.
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Registered trials
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