A review of spline function procedures in R

Background With progress on both the theoretical and the computational fronts the use of spline modelling has become an established tool in statistical regression analysis. An important issue in spline modelling is the availability of user friendly, well documented software packages. Following the i...

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Veröffentlicht in:BMC medical research methodology Jg. 19; H. 1; S. 46 - 16
Hauptverfasser: Perperoglou, Aris, Sauerbrei, Willi, Abrahamowicz, Michal, Schmid, Matthias
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 06.03.2019
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Biostatistics - methods
Blogs
Data analysis
Data Interpretation, Statistical
Functional form of continuous covariates
Generalized linear models
Health Sciences
Humans
Mathematical Computing
Medical research
Medicine
Medicine & Public Health
Models, Theoretical
Multivariable modelling
Open source software
Popularity
Programming Languages
R (Programming language)
Regression analysis
Research Design
Review
Software packages
Spline theory
Statistical methods
Statistical models
Statistical Theory and Methods
statistics and modelling
Statistics for Life Sciences
Survival analysis
Theory of Medicine/Bioethics
Germany
Multivariable modelling
Functional form of continuous covariates
ISSN:1471-2288, 1471-2288
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Zusammenfassung:Background With progress on both the theoretical and the computational fronts the use of spline modelling has become an established tool in statistical regression analysis. An important issue in spline modelling is the availability of user friendly, well documented software packages. Following the idea of the STRengthening Analytical Thinking for Observational Studies initiative to provide users with guidance documents on the application of statistical methods in observational research, the aim of this article is to provide an overview of the most widely used spline-based techniques and their implementation in R. Methods In this work, we focus on the R Language for Statistical Computing which has become a hugely popular statistics software. We identified a set of packages that include functions for spline modelling within a regression framework. Using simulated and real data we provide an introduction to spline modelling and an overview of the most popular spline functions. Results We present a series of simple scenarios of univariate data, where different basis functions are used to identify the correct functional form of an independent variable. Even in simple data, using routines from different packages would lead to different results. Conclusions This work illustrate challenges that an analyst faces when working with data. Most differences can be attributed to the choice of hyper-parameters rather than the basis used. In fact an experienced user will know how to obtain a reasonable outcome, regardless of the type of spline used. However, many analysts do not have sufficient knowledge to use these powerful tools adequately and will need more guidance.
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ISSN:1471-2288
1471-2288
DOI:10.1186/s12874-019-0666-3