R is not only becoming increasingly popular in applied medical research but is also widely used in university teaching. The implementation of spline and GAM fitting routines has a long tradition in R, since some of the earliest routines were written in the S language, which forms the basis of R.
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In this work, we focus on the R Language for Statistical Computing, which has become a hugely popular statistics software since the late 1990’s and which implements a large number of spline functions and modelling options. At, searching for “splines” yielded 2945 results.Īn important prerequisite for spline modelling is the availability of user friendly, well documented software packages.
Similarly, searching for “splines” in the journals Journal of Clinical Oncology and New England Journal of Medicine (just to name a few) resulted in 156 and 63 hits, respectively, showing that spline modelling is not only important in statistical methods development but is also widely used in applied clinical research. For example, searching for the term “splines” at the websites of the journals Statistics in Medicine, Statistical Methods in Medical Research and Biometrical Journal yielded 861, 223 and 189 results, respectively, as of November 24, 2018. non-linear effects of continuous covariates, avoiding distributional assumptions and modelling time-dependent effects in survival analysis, time series, cumulative effects and frequency distributions. Indeed, many new methodological developments in modern biostatistics make use of splines to model smooth functions of interest, including e.g. In particular, splines are regularly used for building explanatory models in clinical research. With progress on both the theoretical and the computational fronts the use of spline modelling has become an established tool in statistical regression analysis. However, many analysts do not have sufficient knowledge to use these powerful tools adequately and will need more guidance.
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In fact an experienced user will know how to obtain a reasonable outcome, regardless of the type of spline used.
Most differences can be attributed to the choice of hyper-parameters rather than the basis used. This work illustrate challenges that an analyst faces when working with data. Even in simple data, using routines from different packages would lead to different results.
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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. Using simulated and real data we provide an introduction to spline modelling and an overview of the most popular spline functions. We identified a set of packages that include functions for spline modelling within a regression framework.
In this work, we focus on the R Language for Statistical Computing which has become a hugely popular statistics software. 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. An important issue in spline modelling is the availability of user friendly, well documented software packages.