Multivariable Analysis: A Practical Guide for Clinicians and Public Health ResearchersCambridge University Press, 10 mrt 2011 Now in its third edition, this highly successful text has been fully revised and updated with expanded sections on cutting-edge techniques including Poisson regression, negative binomial regression, multinomial logistic regression and proportional odds regression. As before, it focuses on easy-to-follow explanations of complicated multivariable techniques. It is the perfect introduction for all clinical researchers. It describes how to perform and interpret multivariable analysis, using plain language rather than complex derivations and mathematical formulae. It focuses on the nuts and bolts of performing research, and prepares the reader to set up, perform and interpret multivariable models. Numerous tables, graphs and tips help to demystify the process of performing multivariable analysis. The text is illustrated with many up-to-date examples from the medical literature on how to use multivariable analysis in clinical practice and in research. |
Inhoudsopgave
| 1 | |
2 Common uses of multivariable models | 14 |
3 Outcome variables in multivariable analysis | 25 |
4 Independent variables in multivariable analysis | 74 |
5 Relationship of independent variables to one another | 88 |
6 Setting up a multivariable analysis | 93 |
7 Performing the analysis | 118 |
8 Interpreting the results | 140 |
Checking the underlying assumptions of the analysis | 162 |
Overige edities - Alles bekijken
Multivariable Analysis: A Practical Guide for Clinicians and Public Health ... Mitchell H. Katz Geen voorbeeld beschikbaar - 2011 |
Multivariable Analysis: A Practical Guide for Clinicians and Public Health ... Mitchell H. Katz Geen voorbeeld beschikbaar - 2011 |
Veelvoorkomende woorden en zinsdelen
adjust analysis of variance ANOVA assess associated baseline bivariate analysis calculate cancer censored observations chi-squared cholesterol clinical clinicians cluster coefficient confidence intervals cut-off decrease drug-eluting stents effect Engl enrollment estimating equations example increases independent variables interaction interval variable interval-independent variable investigators large number linear regression logarithm logistic regression method missing data missing values mixed-effects models mortality multicollinearity multinomial logistic regression multiple dichotomous variables multiple linear regression multiple logistic regression multivariable analysis multivariable model myocardial infarction negative binomial regression number of variables odds ratio outcome variable patients percent persons Poisson regression predict product term propensity score proportional hazards analysis proportional odds regression proportionality assumption randomized relationship relative hazard relative risk residuals right-heart catheterization risk factor risk of death sample Section smoking ST elevations standard errors statistically significant studentized residuals subjects survival Table tion treatment valid warfarin women yes/no yes/present