ArticleExpert review of clinical pharmacology2009
Methods for optimizing statistical analyses in pharmacogenomics research.
Article in Expert review of clinical pharmacology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Genotypic and Phenotypic Factors Influencing Drug Response in Mexican Patients With Type 2 Diabetes Mellitus.Frontiers in pharmacology · 2018Article
- TESTING POPULATION-SPECIFIC QUANTITATIVE TRAIT ASSOCIATIONS FOR CLINICAL OUTCOME RELEVANCE IN A BIOREPOSITORY LINKED TO ELECTRONIC HEALTH RECORDS: LPA AND MYOCARDIAL INFARCTION IN AFRICAN AMERICANS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016Article
- Statistical analysis of big data on pharmacogenomics.Advanced drug delivery reviews · 2013Article
- Article
- Statistical Optimization of Pharmacogenomics Association Studies: Key Considerations from Study Design to Analysis.Current pharmacogenomics and personalized medicine · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Pharmacogenomics is a rapidly developing sector of human genetics research with arguably the highest potential for immediate benefit. There is a considerable body of evidence demonstrating that variability in drug-treatment response can be explained in part by genetic variation. Subsequently, much research has ensued and is ongoing to identify genetic variants associated with drug-response phenotypes. To reap the full benefits of the data we collect we must give careful consideration to the study population under investigation, the phenotype being examined and the statistical methodology used in data analysis. Here, we discuss principles of study design and optimizing statistical methods for pharmacogenomic studies when the outcome of interest is a continuous measure. We review traditional hypothesis testing procedures, as well as novel approaches that may be capable of accounting for more variance in a quantitative pharmacogenomic trait. We give examples of studies that have employed the analytical methodologies discussed here, as well as resources for acquiring software to run the analyses.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.