title: [A review of correlation, regression and multiple factors analysis methods.].
abstract: In clinical research, the relationships between two factors or among multiple factors are frequently explored. Dependence and covariation are two kinds of relationships between two factors. Regression and correlation analysis are the appropriate methods in measuring and evaluating those factors. Multiple analysis methods are widely used in the studies of risk factors among diseases, data analyses of diagnostic tests, effects evaluation of a prevention and treatment, analyses of prognosis, etc. Multiple analysis methods could interpret the influences of each factor more specifically than single factor analysis, and provide more possibilities to improve the quality of a research. In this review, the fundamental principle and utilities on clinical research of several statistical methods are introduced in this paper, such as linear correlation, linear regression, rank regression, rank correlation, multiple linear regression and logistic regression. It intends to help the clinicians and researchers to select the appropriate statistical methods to collect and analyze the data.
authors.affiliation: School of Public Health,Shanghai Second Medical University, Shanghai 200025, China. smile567@sohu.com
authors.forename: Gang
authors.initials: G
authors.lastname: Xu
authors.forename: Yong
authors.initials: Y
authors.lastname: Cai
authors.forename: Xin
authors.initials: X
authors.lastname: Song
authors.forename: Yang
authors.initials: Y
authors.lastname: Wang
mesh_terms: D016021:Epidemiologic Studies; D006801:Humans; D016015:Logistic Models; D011379:Prognosis; D012107:Research Design; D012307:Risk Factors