Abstract
At present, regression methods are an essential component of the analysis of data of any medical study describing the relationship between an outcome variable, or dependent variable, and one or more explanatory independent variables.
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References
Kleinbaum DG, Kupper LL, Muller KE. Applied regression analysis and other multivariate methods. Boston, PWS-Kent, 1988.
Cornfield J, Gordon T, Smith WW. Quantal response curves for experimentally uncontrolled variables. Bull Int Statis Ins 1961; 38: 97ā115.
Cornfield J. Joint dependence of risk of coronary heart disease on serum cholesterol and systolic blood presure: a discriminant function analysis. Fed Proc 1962, 2: 58ā61.
Cox DR. Analysis of binary data. London, Methuen, 1970.
Gordon T. Hazards in the use of the logistic function. J Chron Dis 1974; 27: 97ā102.
Breslow NE, Day NE. Statistical methods in cancer research. Volume 1: The analysis of case-control studies. Lyon, International Agency for Research on Cancer, 1982.
Kleinbaum DG, Kupper LL, Morgenstern H. Epidemiologic research. Principles and quantitative methods. New York, Van Nostrand Reinhold, 1982.
Hosmer DW, Lemeshow S. Applied logistic regression. New York, John Wiley and Sons, 1989.
Engelman L. Stepwise logistic regression. In: Dixon WJ, Ed. BMDP statistical software. Vol 2. Berkeley, University of California Press, 1988; 941ā969.
Sas-Stat procedures. Cary, Sas Institute Inc, 1988.
Epilog, Plus. Statistical package for epidemiology and clinical trials. Pasadena, Epicenter Software, 1990.
Egret. Epidemiological graphics, estimation, and testing package. Seattle, Statistics and Epidemiological Research Corporation, 1990.
Stata. Los Angeles, Computing Resource Center, 1990.
Walker SH, Duncan DB. Estimation of the probability of an event as a function of several independent variables. Biometrics 1967; 54: 167ā179.
Deubner DC, Wilkinson WE, Helms MJ, Tyroler HA, Hames CG. Logistic estimation of death attributable to risk factors for cardiovascular disease in Evans County, Georgia. Am J Epidemiol 1980; 112: 135ā143.
Mc Donough JR, Hames CG, Stulb SC, Garrison GE. Coronary heart disease among Negroes and Whites in Evans County, Georgia. J Chron Dis 1965; 18: 443ā468.
Brown KA, Osbakken M, Boucher CA, Strauss HW, Pohost GM, Okada RD. Positive exercise thallium-201 test responses inpatients with less than 50% maximal coronary stenosis: Angiographic and clinical predictors. Am J Cardiol 1985; 55: 54ā57.
Nienaber CA, Hiller S, Spielmann RP, Geiger M, Kuck CH. Syncope in hypertrophic cardiomyopathy: multivariate analysis of prognosis determinants. J Am Coll Cardiol 1990; 15: 948ā955.
Vatteront PJ, Bailey KR, Hammill SC. Improving the predictive ability of the signal-av-eraged electrocardiogram with a linear logistic model incorporanting clinical variables. Circulation 1990; 81: 797ā804.
Candell-Riera J, Permanyer-Miralda G, Castell J, et al. Uncomplicated first myocardial infarction: strategy for comprehensive prognostic studies. J Am Coll Cardiol 1991; 18: 1207ā1219.
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Ā© 1994 Springer Science+Business Media Dordrecht
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VaquƩ-Rafart, J. (1994). Uses of multiple logistic regression. In: Candell-Riera, J., Ortega-Alcalde, D. (eds) Nuclear Cardiology in Everyday Practice. Developments in Cardiovascular Medicine, vol 146. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-1984-9_20
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DOI: https://doi.org/10.1007/978-94-011-1984-9_20
Publisher Name: Springer, Dordrecht
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