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Biostatistique (5) .............. . 1 Santé publique (1).............. . 2 Science: Recherche (1) ...... . 3
B IOSTATISTIQUE / S TATISTIQUES
Volume 10, numéro 6-7
Rizopoulos D. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R. Boca Raton: CRC Press; 2012. (CRC Biostatistics Series) DIM3017 / H. Jacqmin / V. Rondeau In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-toEvent Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author. All the R code used in the book is available at: http://jmr.r-forge.r-project.org/ Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer; 2010. (Statistics for Biology and Health) DIM3018 / H. Jacqmin / V. Rondeau This book provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but these innovations are insufficiently applied in medical research. Old-fashioned, data hungry methods are often used in data sets of limited size, validation of predictions is not done or done simplistically, and updating of previously developed models is not considered. A sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. Clinical prediction models presents a practical checklist with seven steps that need to be considered for development of a valid prediction model. These include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formats. The steps are illustrated with many small case-studies and R code, with data sets made available in the public domain. The book further focuses on generalizability of prediction models, including patterns of invalidity that may be encountered in new settings, approaches to updating of a model, and comparisons of centers after case-mix adjustment by a prediction model. The text is primarily intended for clinical epidemiologists and biostatisticians. It can be used as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. It is beneficial if readers are familiar with common statistical models in medicine: linear regression, logistic regression, and Cox regression. The book is practical in nature. But it provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. In this era of evidence-based medicine, randomized clinical trials are the basis for assessment of treatment efficacy. Prediction models are key to individualizing diagnostic and treatment decision making. Ewout Steyerberg (1967) is Professor of Medical Decision Making, in particular prognostic modeling, at Erasmus MC–University Medical Center Rotterdam, the Netherlands. His work on prediction models was stimulated by various research grants including a fellowship from the Royal Netherlands Academy of Arts and Sciences. He has published over 250 peer-reviewed articles in collaboration with many clinical researchers, both in methodological and medical journals.
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© Bibliothèque ISPED Lid Hjort N, Holmes C, Müller P, Walker SG, editors. Bayesian Nonparametrics. Cambridge: Cambridge University Press; 2010.(Series in Statistical and Probabilistic Mathematics No 28) DIM3024 / B. Hejblum Bayesian nonparametrics works – theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics. Hair JF, Thomas G. A primer on partial least squares structural equations modeling (PLS-SEM). London: Sage; 2013. DIM3013 / R. Thiébaut A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), by Hair, Hult, Ringle, and Sarstedt, provides a concise yet very practical guide to understanding and using PLS structural equation modeling (PLS-SEM). PLS-SEM is evolving as a statistical modeling technique and its use has increased exponentially in recent years within a variety of disciplines, due to the recognition that PLS-SEM’s distinctive methodological features make it a viable alternative to the more popular covariance-based SEM approach. This text—the only comprehensive book available to explain the fundamental aspects of the method—includes extensive examples on SmartPLS software, and is accompanied by multiple data sets that are available for download from the accompanying website (www.pls-sem.com). Hoyle RH, editor. Handbook of Structural Equation Modeling. New-York: Guilford Press; 2012. DIM3025 / M.Q Picat The first comprehensive structural equation modeling (SEM) handbook, this accessible volume presents both the mechanics of SEM and specific SEM strategies and applications. The editor, contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results.
PUBLIQUE Ridde V. L’accès aux soins de santé en Afrique de l’Ouest. Au-delà des idéologies et des idées reçues. Montréal Les Presses de l'Université de Montréal; 2012. DIM3019 / B. Chanfreau «Ce livre est à ce jour le meilleur corpus de connaissances sur les enjeux des barrières financières à l’accès aux soins. Il résume excellemment les stratégies fondées sur l’équité qui lèvent ces barrières.» Nicolas Meda, médecin, enseignant-chercheur, Burkina Faso
© Bibliothèque ISPED
S CIENCE: E DUCATION-R ECHERCHE Stephan P. How Economics Shapes Science. Harvard University Press; 2012. DIM3014 / G. Chêne ( CIC-EC7) The beauty of science may be pure and eternal, but the practice of science costs money. And scientists, being human, respond to incentives and costs, in money and glory. Choosing a research topic, deciding what papers to write and where to publish them, sticking with a familiar area or going into something new—the payoff may be tenure or a job at a highly ranked university or a prestigious award or a bump in salary. The risk may be not getting any of that.
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