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references.bib
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@Article{Plevris2021,
author = {Nikolas Plevris and James Fulforth and Mathew Lyons and Spyros I. Siakavellas and Philip W. Jenkinson and Cher S. Chuah and Laura Lucaciu and Rebecca J. Pattenden and Ian D. Arnott and Gareth-Rhys Jones and Charlie W. Lees},
journal = {Clinical Gastroenterology and Hepatology},
title = {Normalization of fecal calprotectin within 12 months of diagnosis is associated with reduced risk of disease progression in patients with {Crohn's} disease},
year = {2021},
month = {sep},
number = {9},
pages = {1835--1844.e6},
volume = {19},
doi = {10.1016/j.cgh.2020.08.022},
publisher = {Elsevier {BV}},
}
@article{Henriksen2007,
doi = {10.1080/00365520601076124},
url = {https://doi.org/10.1080%2F00365520601076124},
year = 2007,
month = {jan},
publisher = {Informa {UK} Limited},
volume = {42},
number = {5},
pages = {602--610},
author = {Magne Henriksen and J{\o}rgen Jahnsen and Idar Lygren and Erling Aadland and Tom Schulz and Morten H. Vatn and Bj{\o}rn Moum and THE IBSEN STUDY GROUP},
title = {Clinical course in Crohn{\textquotesingle}s disease: Results of a five-year population-based follow-up study (the {IBSEN} study)},
journal = {Scandinavian Journal of Gastroenterology}
}
@article{runge,
author = {Platte, Rodrigo B. and Driscoll, Tobin A.},
title = {Polynomials and Potential Theory for Gaussian Radial Basis Function Interpolation},
journal = {SIAM Journal on Numerical Analysis},
volume = {43},
number = {2},
pages = {750-766},
year = {2005},
doi = {10.1137/040610143},
URL = {https://doi.org/10.1137/040610143},
eprint = {https://doi.org/10.1137/040610143},
abstract = { We explore a connection between Gaussian radial basis functions and polynomials. Using standard tools of potential theory, we find that these radial functions are susceptible to the Runge phenomenon, not only in the limit of increasingly flat functions, but also in the finite shape parameter case. We show that there exist interpolation node distributions that prevent such phenomena and allow stable approximations. Using polynomials also provides an explicitinterpolation formula that avoids the difficulties of inverting interpolation matrices, while not imposing restrictions on the shape parameter or number of points.}
}
@Article{Proust-Lima2017,
title = {Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The {R} Package {lcmm}},
author = {C\'ecile Proust-Lima and Viviane Philipps and Benoit Liquet},
journal = {Journal of Statistical Software},
year = {2017},
volume = {78},
number = {2},
pages = {1--56},
doi = {10.18637/jss.v078.i02},
}
@Book{Harrell2015,
author = {Frank E. Harrell ,},
publisher = {Springer International Publishing},
title = {Regression Modeling Strategies},
year = {2015},
doi = {10.1007/978-3-319-19425-7},
}
@article{Fornberg2007,
title = {The Runge phenomenon and spatially variable shape parameters in RBF interpolation},
journal = {Computers & Mathematics with Applications},
volume = {54},
number = {3},
pages = {379-398},
year = {2007},
issn = {0898-1221},
doi = {https://doi.org/10.1016/j.camwa.2007.01.028},
url = {https://www.sciencedirect.com/science/article/pii/S0898122107002210},
author = {Bengt Fornberg and Julia Zuev},
keywords = {Radial basis functions, RBF, Runge phenomenon, Shape parameter, Conditioning},
abstract = {Many studies, mostly empirical, have been devoted to finding an optimal shape parameter for radial basis functions (RBF). When exploring the underlying factors that determine what is a good such choice, we are led to consider the Runge phenomenon (RP; best known in cases of high order polynomial interpolation) as a key error mechanism. This observation suggests that it can be advantageous to let the shape parameter vary spatially, rather than assigning a single value to it. Benefits typically include improvements in both accuracy and numerical conditioning. Still another benefit arises if one wishes to improve local accuracy by clustering nodes in selected areas. This idea is routinely used when working with splines or finite element methods. However, local refinement with RBFs may cause RP-type errors unless we use a spatially variable shape paremeter. With this enhancement, RBF approximations combine freedom from meshes with spectral accuracy on irregular domains, and furthermore permit local node clustering to improve the resolution wherever this might be needed.}
}
@article{Elhakeem2022,
abstract = {Longitudinal data analysis can improve our understanding of the influences on health trajectories across the life-course. There are a variety of statistical models which can be used, and their fitting and interpretation can be complex, particularly where there is a nonlinear trajectory. Our aim was to provide an accessible guide along with applied examples to using four sophisticated modelling procedures for describing nonlinear growth trajectories.},
author = {Elhakeem, Ahmed and Hughes, Rachael A. and Tilling, Kate and Cousminer, Diana L. and Jackowski, Stefan A. and Cole, Tim J. and Kwong, Alex S. F. and Li, Zheyuan and Grant, Struan F. A. and Baxter-Jones, Adam D. G. and Zemel, Babette S. and Lawlor, Deborah A.},
date = {2022/03/15},
date-added = {2022-03-18 15:03:41 +0000},
date-modified = {2022-03-18 15:03:41 +0000},
doi = {10.1186/s12874-022-01542-8},
id = {Elhakeem2022},
isbn = {1471-2288},
journal = {BMC Medical Research Methodology},
number = {1},
pages = {68},
title = {Using linear and natural cubic splines, SITAR, and latent trajectory models to characterise nonlinear longitudinal growth trajectories in cohort studies},
url = {https://doi.org/10.1186/s12874-022-01542-8},
volume = {22},
year = {2022},
bdsk-url-1 = {https://doi.org/10.1186/s12874-022-01542-8}}
@Article{Peto1972,
author = {Richard Peto and Julian Peto},
journal = {Journal of the Royal Statistical Society. Series A (General)},
title = {Asymptotically Efficient Rank Invariant Test Procedures},
year = {1972},
number = {2},
pages = {185},
volume = {135},
doi = {10.2307/2344317},
publisher = {{JSTOR}},
}