title: A marginal rank-based inverse normal transformation approach to comparing multiple clinical trial endpoints.
abstract: The increase in incidence of obesity and chronic diseases and their health care costs have raised the importance of quality diet on the health policy agendas. The healthy eating index is an important measure for diet quality which consists of 12 components derived from ratios of dependent variables with distributions hard to specify, measurement errors and excessive zero observations difficult to model parametrically. Hypothesis testing involving data of such nature poses challenges because the widely used multiple comparison procedures such as Hotelling's T(2) test and Bonferroni correction may suffer from substantial loss of efficiency. We propose a marginal rank-based inverse normal transformation approach to normalizing the marginal distribution of the data before employing a multivariate test procedure. Extensive simulation was conducted to demonstrate the ability of the proposed approach to adequately control the type I error rate as well as increase the power of the test, with data particularly from non-symmetric or heavy-tailed distributions. The methods are exemplified with data from a dietary intervention study for type I diabetic children. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
authors.affiliation: Department of Statistics, George Washington University, Washington DC, 20052, U.S.A.
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Cai
authors.affiliation: School of Management and Economics, Beijing Institute of Technology, Haidian District, Beijing, 100081, China.
authors.forename: Huiyun
authors.initials: H
authors.lastname: Li
authors.affiliation: Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, MD, 20852, U.S.A.
authors.forename: Aiyi
authors.initials: A
authors.lastname: Liu
mesh_terms: D002908:Chronic Disease / Q000517:prevention & control; D002986:Clinical Trials as Topic; D023381:Endpoint Determination; D006801:Humans; D009765:Obesity / Q000517:prevention & control; D012107:Research Design
keywords: clinical trials; multiple endpoints; power of test; rank-based transformation; ratio of dependent variables; type I error