title: Variance components and their implications for statistical information in medical data.
abstract: We discuss general issues concerning the design and analysis of medical experiments involving repeated measures or hierarchical groupings of subjects within larger study units. Depending on the types of questions being investigated, the correlations induced by clustering can have dramatic impact on the effective sample size. The unique aspects of such experiments must be accounted for during analysis and during interpretation of the results. We illustrate these issues by using a variance components model to investigate the role of leadership in medical practice.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, Boston, MA.
authors.forename: E J
authors.initials: EJ
authors.lastname: Orav
authors.forename: T A
authors.initials: TA
authors.lastname: Louis
authors.forename: R H
authors.initials: RH
authors.lastname: Palmer
authors.forename: E A
authors.initials: EA
authors.lastname: Wright
mesh_terms: D000553:Ambulatory Care / Q000592:standards*; D000704:Analysis of Variance; D001499:Bayes Theorem; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D007857:Leadership; D009010:Monte Carlo Method; D010819:Physician's Role; D011785:Quality Assurance, Health Care / Q000706:statistics & numerical data*; D012494:Sampling Studies