Multivariable ordinal logistic regression of factors associated with back pain intensity, adults 50+ years, pooled, SAGE Wave 1 (N = 8,815).Jennifer Stewart WilliamsNawi NgKarl PeltzerAlfred YawsonRichard BiritwumTamara MaximovaFan WuPerianayagam Arokiasamy...
in a reproducible manner in the context of regression analyses with a continuous, binary, ordinal or count-type outcome variable. While we focus on descriptive or predictive research questions, many aspects discussed here will also extend to explanatory models that seek to estimate a causal effect ...
9 1.2.3 DisadvantagesofFractionalPolynomialModelling,9 1.2.4 ControllingModelComplexity,10 1.3 TypesofRegressionModelConsidered,10 1.3.1 Normal-ErrorsRegression,10 1.3.2 LogisticRegression,12 1.3.3 CoxRegression,12 1.3.4 GeneralizedLinearModels,14 1.3.5 LinearandAdditivePredictors,14 1.4 RoleofResiduals,...
[50] used hierarchical logistic regression (possible or probable anxiety, Y/N); again, the multivariable model only included predictors that demonstrated bivariate associations (p≤ 0.05). Hopwood et al. [51] used proportional odds logistic regression (for ordinal data; normal, borderline or case ...
Univariable and multivariable ordinal logistic regression for household economic poverty among the 200 trichiasis cases only.Esmael, HabtamuTariku, WondieSintayehu, AwekeZerihun, TadesseMulat, ZerihunZebideru, ZewdieKelly, CallahanPaul, M. Emerson...
chapters on polytomous and ordinal logistic regression (Chapter 23) and sample size determination (Chapter 27). chapters on maximum likelihood (ML) estimation (Chapter 21), and analysis of correlated data (Chapter 25, 26). updated content, with new exercises added to several chapters. numerous ...
Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. New York (NY): Springer-Verlag; 2001.Harrell FE Jr. Multivariable Modeling Strategies. Regression modelling strategies: with applications to Linear Models, Logistic and Ordinal regression, and ...