Selection of important variables and determination of functional form for continuous predictors in multivariable model building

By Willi Sauerbrei, Patrick Royston, Harald Binder. Statistics in Medicine, 2007

1,050 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b393e63bf733dd36e1dddde24dafb8128115bf18

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In Analytical Chemistry

Willi Sauerbrei

In Statistics and Probability

Citations in the corpus

Cross-Validation, Shrinkage and Variable Selection in Linear Regression Revisited (2013, background): “At least interpretability, model complexity and practical usefulness are relevant [6].”
Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research (2013, background): “Important issues to be addressed include dealing with missing data [26], modelling continuous prognostic factors [27,28], the complexity of the model [29], and checking the model assumptions.”
Assessing the reporting of categorised quantitative variables in observational epidemiological studies (2017, background): “There are no clear procedures to decide on the choice and number of confounders and other variables when investigating exposures and outcome relationships [33].”
flexsurv: A Platform for Parametric Survival Modeling in R. (2016, background): “See e.g., Sauerbrei et al. (2007), and several other publications by the same authors, for applications and discussion of their advantages over splines.”
Dose‐response analyses using restricted cubic spline functions in public health research (2010, background): “How to take into account a continuous exposure in regression models has been widely described in the literature [1, 2].”
Applications of structural equation modeling (SEM) in ecological studies: an updated review (2016, methodology): “Variable selection is also called the feature selection—a process of selecting the most relevant variables to prevent overfitting (Sauerbrei et al. 2007; Murtaugh 2009; Burnham and Anderson 2002)—and is also a required procedure for both PLS-SEM and exploratory factor analysis.”
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration (2012, methodology): “More often, however, many candidate variables are available and some type of variable selection procedure is sensible in order to derive simpler models which are easier to interpret and may be more generally useful [123,146].”