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Regression Modeling Strategies
Revista Española de Cardiología (English Edition), 2011Multivariable regression models are widely used in health science research, mainly for two purposes: prediction and effect estimation. Various strategies have been recommended when building a regression model: a) use the right statistical method that matches the structure of the data; b) ensure an appropriate sample size by limiting the number of ...
Nunez, E, Steyerberg, Ewout, Nunez, J
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International Statistical Review / Revue Internationale de Statistique, 1992
Summary Various diagnostics for generalized linear models are reviewed and extended to more general models. These include some models for censored and grouped data, and regressions that are nonlinear, or where the response does not have an exponential family distribution.
Davison, A. C., Tsai, C.-L.
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Summary Various diagnostics for generalized linear models are reviewed and extended to more general models. These include some models for censored and grouped data, and regressions that are nonlinear, or where the response does not have an exponential family distribution.
Davison, A. C., Tsai, C.-L.
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Journal of Clinical Epidemiology, 2006
Ordinal scales often generate scores with skewed data distributions. The optimal method of analyzing such data is not entirely clear. The objective was to compare four statistical multivariable strategies for analyzing skewed health-related quality of life (HRQOL) outcome data.
Colleen M, Norris +6 more
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Ordinal scales often generate scores with skewed data distributions. The optimal method of analyzing such data is not entirely clear. The objective was to compare four statistical multivariable strategies for analyzing skewed health-related quality of life (HRQOL) outcome data.
Colleen M, Norris +6 more
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(Non) linear regression modeling [PDF]
, 2004 We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1,…,Yl), l ∈ N, which are explained by a model, and independent (exogenous, explanatory) variables X = (X1,…,Xp),p ∈ N, which explain or predict the dependent variables by means of the model. Such
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
exaly
2020
This chapter evaluates regression models, focusing on the normal linear regression model. The normal linear regression model establishes a relationship between a quantitative response (also called outcome or dependent) variable, assumed to be normally distributed, and one or more explanatory (also called regression, predictor, or independent) variables
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This chapter evaluates regression models, focusing on the normal linear regression model. The normal linear regression model establishes a relationship between a quantitative response (also called outcome or dependent) variable, assumed to be normally distributed, and one or more explanatory (also called regression, predictor, or independent) variables
openaire +1 more source