Multivariable Analysis: An Introduction
Yale University Press, 1996 - 613 páginas
For physicians, nurses, and all others who confront increasingly complex statistical methods in research, Dr. Alvan R. Feinstein offers welcome help. He has written a highly readable explanation of the uses and significance of multivariable analysis in medical and life science research.
A physician with wide experience in both clinical work and research, Dr. Feinstein succeeds in demystifying arcane vocabulary and unfamiliar mathematics. His book is a roadmap taking the reader from the basics of univariate and bivariate statistics, through methods of converting information into data coded for computers, and on to multivariable statistics. Dr. Feinstein focuses on four methods common in the medical literature: multiple linear regression, multiple logistic regression, discriminant function analysis, and Cox regression. He also discusses such new methods as recursive partitioning and conjunctive consolidation. Clarifying examples and illustrations abound, and an accompanying floppy disk helps to facilitate learning.
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Preparing for Multivariable Analysis
Basic Strategies of Targeted Algebraic Methods
Multivariable explorations and reductions
Evaluations and illustrations of multiple linear regression
Regression for Nondimensional Targets
Evaluations and illustrations of logistic regression
Proportional hazards analysis Cox regression
Evaluations and illustrations of Cox regression
Discriminant function analysis
Illustrations of discriminant function analysis
Partv TargetedCluster Methods
Additional discussion and conclusions
able additional algebraic analysis approach arrangement basic becomes binary BMDP calculated called cell Chapter checked chi-square cited classification clinical clusters coding coefficients combinations contains correlation corresponding curve decisions determined discriminant discussed distinctive distribution effect entered error estimates event examined example expressed format formula four function goal gradient hazard illustrative impact important independent variables indicate individual interaction interval likelihood linear regression logistic regression mathematical mean methods multiple multivariable myocardial infarction noted observed occur particular patients pattern persons points predictive printout probability problem procedure produce proportional rates ratio reduction regression relatively removed residual score separate shown shows significant simple single square stage standardized statistical step stochastic strategy survival symptom Table target variable tion treatment usually vari variance weight zones
Página 592 - MB et al.: The American College of Rheumatology, 1990 Criteria for the Classification of Fibromyalgia. Report of the Multicenter Criteria Committee.
Breast Cancer: Translational Therapeutic Strategies
Gary H. Lyman,Harold J. Burstein
Sin vista previa disponible - 2007