Some statistical and regulatory issues in the evaluation of genetic and genomic tests.
The genomics revolution is reverberating throughout the worlds of pharmaceutical drugs, genetic testing and statistical science. This revolution, which uses single nucleotide polymorphisms (SNPs) and gene expression technology, including cDNA and oligonucleotide microarrays, for a range of tests from home-brews to high-complexity lab kits, can allow the selection or exclusion of patients for therapy (responders or poor metabolizers). The wide variety of US regulatory mechanisms for these tests is discussed. Clinical studies to evaluate the performance of such tests need to follow statistical principles for sound diagnostic test design. Statistical methodology to evaluate such studies can be wide ranging, including receiver operating characteristic (ROC) methodology, logistic regression, discriminant analysis, multiple comparison procedures resampling, Bayesian hierarchical modeling, recursive partitioning, as well as exploratory techniques such as data mining. Recent examples of approved genetic tests are discussed.
Campbell
Gregory
G
Division of Biostatistics, Center for Devices and Radiological Health, US Food and Drug Administration, Rockville, Maryland 20850, USA. gxc@cdrh.fda.gov
D000818:Animals; D003627:Data Interpretation, Statistical; D023281:Genomics / Q000331:legislation & jurisprudence* / Q000706:statistics & numerical data*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D010597:Pharmacogenetics; D014481:United States; D014486:United States Food and Drug Administration