title: Controlling false positive rates in prognostic factor analyses with small samples.
abstract: Exploratory data analyses in medical research usually involve many potential risk factors. Typically, one performs numerous hypothesis tests to identify variables that are of prognostic value. Because of the multiplicity of tests, one must control the overall false positive rate. The Bonferroni adjustment is simple to use, but may be overly conservative when applied to correlated tests. We propose an exact adjustment method, based on the joint permutational distributions of the test statistics, in settings where the acquired sample size only allows analysis of a single feature at a time. We demonstrate our method with two examples.
authors.affiliation: Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38101, USA.
authors.forename: Q
authors.initials: Q
authors.lastname: Liu
authors.forename: Y
authors.initials: Y
authors.lastname: Li
authors.forename: J M
authors.initials: JM
authors.lastname: Boyett
mesh_terms: D000293:Adolescent; D000328:Adult; D002648:Child; D002869:Chromosome Aberrations / Q000235:genetics; D002880:Chromosomes, Human, Pair 11; D004268:DNA-Binding Proteins / Q000235:genetics; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D005260:Female; D015321:Gene Rearrangement / Q000235:genetics; D011495:Histone-Lysine N-Methyltransferase; D006801:Humans; D008297:Male; D008432:Mathematical Computing; D015233:Models, Statistical; D051788:Myeloid-Lymphoid Leukemia Protein; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000235:genetics / Q000401:mortality; D011379:Prognosis; D011519:Proto-Oncogenes; D012044:Regression Analysis; D012984:Software; D016019:Survival Analysis; D014157:Transcription Factors
chemical_list: D004268:DNA-Binding Proteins; C497422:KMT2A protein, human; D014157:Transcription Factors; D051788:Myeloid-Lymphoid Leukemia Protein; D011495:Histone-Lysine N-Methyltransferase