A vibrant, in-depth survey of the techniques and traditions of patchwork around the world. What do Korean bojagi wrapping cloths, Cameroonian Bamileke boubous, Peruvian montera hats and Hungarian cifraszur shepherd cloaks have in common? Each is made using the ancient technique of patchwork - the art of juxtaposing fabrics and motifs to create blankets, clothes, accessories and more. Catherine Legrand, who has spent many years travelling and researching textiles, has a deep knowledge of the techniques and traditions that characterize patchwork. Pieced together much like the gorgeous textiles it portrays, her beautifully illustrated book features over 300 dazzling photographs of patchwork around the world. She takes the reader from Europe and the Americas to Africa and Asia, where these ancient traditions still survive and patchwork is part of the fabric of everyday life. Textile artists, patchwork enthusiasts and travellers alike will find an endless source of inspiration on the pages of this celebration of the beauty and diversity of patchwork.

Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome. Features Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome Uses consistent notation throughout the book for the different techniques presented Explains in which situation each of these models should be used, and how they are linked to specific research questions Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.

Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome. Features Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome Uses consistent notation throughout the book for the different techniques presented Explains in which situation each of these models should be used, and how they are linked to specific research questions Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.

While the primary objective of multicenter clinical trials is to compare treatments for a specific disease, they also contain a lot of relevant additional information. For example, it may be interesting to investigate heterogeneity in outcome and in treatment effect over centers. Considering a frailty model including a random center effect and a random treatment by center interaction term, we demonstrate how to quantify this heterogeneity, to interpret it based on medically relevant quantities, and to identify center and patient specific factors explaining it. Based on the same idea, we also propose to study heterogeneity in prognostic index effect over centers, and to use this information to get a new insight in the validation of prognostic indices. Various approaches are discussed to fit such a frailty model, and a Bayesian approach based on the Laplace approximation is detailled. We illustrate this research using data from an EORTC multicenter clinical trial for breast cancer and from a pooled database of seven EORTC clinical trials in bladder cancer patients.