BookDOI
Regression Modeling Strategies
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TLDR
Regression models are frequently used to develop diagnostic, prognostic, and health resource utilization models in clinical, health services, outcomes, pharmacoeconomic, and epidemiologic research, and in a multitude of non-health-related areas.Abstract:
Regression models are frequently used to develop diagnostic, prognostic, and health resource utilization models in clinical, health services, outcomes, pharmacoeconomic, and epidemiologic research, and in a multitude of non-health-related areas. Regression models are also used to adjust for patient heterogeneity in randomized clinical trials, to obtain tests that are more powerful and valid than unadjusted treatment comparisons.read more
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Journal ArticleDOI
Generalized linear mixed models: a practical guide for ecology and evolution
Benjamin M. Bolker,Mollie Elizabeth Brooks,Connie J. Clark,Shane W. Geange,John R. Poulsen,M. Henry H. Stevens,Jada-Simone S. White +6 more
TL;DR: The use (and misuse) of GLMMs in ecology and evolution are reviewed, estimation and inference are discussed, and 'best-practice' data analysis procedures for scientists facing this challenge are summarized.
Journal ArticleDOI
Random effects structure for confirmatory hypothesis testing: Keep it maximal
TL;DR: It is argued that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades, and it is shown thatLMEMs generalize best when they include the maximal random effects structure justified by the design.
Journal ArticleDOI
Mixed-effects modeling with crossed random effects for subjects and items
TL;DR: In this article, the authors provide an introduction to mixed-effects models for the analysis of repeated measurement data with subjects and items as crossed random effects, and a worked-out example of how to use recent software for mixed effects modeling is provided.
Journal ArticleDOI
Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples
TL;DR: Methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero are described, thereby allowing one to determined the range of standardized differences that are plausible with the propensity score model having been correctly specified.
Journal ArticleDOI
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Hugo J.W.L. Aerts,Emmanuel Rios Velazquez,Ralph T.H. Leijenaar,Chintan Parmar,Patrick Grossmann,Sara Carvalho,Sara Cavalho,Johan Bussink,René Monshouwer,Benjamin Haibe-Kains,Derek H. F. Rietveld,Frank J. P. Hoebers,Michelle M. Rietbergen,C. René Leemans,Andre Dekker,John Quackenbush,Robert J. Gillies,Philippe Lambin +17 more
TL;DR: The data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer, which may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.
References
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Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
BookDOI
Modern Applied Statistics with S
W. N. Venables,Brian D. Ripley +1 more
TL;DR: A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods.
Book
Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.