Multivariable logistic regression model
[DOC File]Lippincott Williams & Wilkins
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b The model was adjusted for age at diagnosis, sex, race/ethnicity, health plan, tumor grading, census tract education, modified Charlson comorbidities, and AJCC stage. c The propensity of receiving BCP was estimated using a multivariable logistic regression model that included age at diagnosis, sex, race/ethnicity, health plan, tumor grading ...
[DOCX File]EFFECTIVENESS OF RIGHT HEART CATHETERIZATION IN …
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A propensity score for RHC was constructed using multivariable logistic regression. Case-matching and multivariable regression modeling techniques were used to estimate the association of RHC with specific outcomes after adjusting for treatment selection using the propensity score.
[DOC File]Welcome to D-Scholarship@Pitt - D-Scholarship@Pitt
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A univariate logistic regression was first done on the risk factors and the other mentioned variables to determine association. All variables with a p-value of less than 0.2 were then entered into a stepwise multivariable logistic regression model.
[DOCX File]Total, direct and indirect effects
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Hence, multiple regression, logistic regression, Poisson regression, and many other conventional models can be estimated using the generalized linear model. For time-to-event data, the Cox regression model (2) is probably the most commonly used approach today, although parametric techniques also appear with relative frequency.
[DOC File]1 - Scottish Government
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Multivariable logistic regression models used these imputed data sets to predict the likelihood of a poor father-child relationship for any one risk factor, after controlling for other factors in the model.
[DOC File]Table 3 - Cambridge University Press
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Table A1. Multivariable logistic regression model of factors associated with suicidal behavior in 3284 mood disorder subjects. Factors OR [95% CI] 2 p-value
[DOC File]Model Selection in R - Donald Bren School of Information ...
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The forward stepwise regression procedure identified the model which included the two predictors Holiday and Cases, but not Costs, as the one which produced the lowest value of AIC. To use the same procedure in the backward direction, the command is much simpler, since the full model is the base model.
[DOC File]Modeling Observational Data - Duke University
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The importance of events per independent variable in multivariable analysis, II: accuracy and precision of regression estimates. J Clin Epidemiol 1995; 48: 1503–10 . Peduzzi PN, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996; 49 ...
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None were significant in this model and therefore not included. Notes: Odds ratios (OR) and confidence intervals (CI) are only reported in the multivariable logistic regression for variables included in the model. Author: Khoong, Elaine Created Date: 10/02/2018 21:47:00
[DOCX File]Mass.Gov
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Aug 31, 2017 · A multivariable logistic regression model was used to identify characteristics associated with heroin abuse or dependence. R. esults: Annual average rates of past-year heroin use increased from 1.6 per 1,000 persons aged ≥12 years in 2002–2004 to 2.6 per 1,000 in 2011–2013. Rates of heroin abuse or dependence were strongly positively ...
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