{"product_id":"dynamic-prediction-in-clinical-survival-analysis-1","title":"Dynamic Prediction in Clinical Survival Analysis","description":"\u003cp\u003eThere is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime after diagnosis or treatment. In contrast, Dynamic \u003cstrong\u003ePrediction in Clinical Survival Analysis\u003c\/strong\u003e focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models. \u003c\/p\u003e\u003cp\u003eDesigned to be useful to applied statisticians and clinical epidemiologists, each chapter in the book has a practical focus on the issues of working with real life data. Chapters conclude with additional material either on the interpretation of the models, alternative models, or theoretical background. The book consists of four parts:\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003e \u003cem\u003ePart I\u003c\/em\u003e deals with prognostic models for survival data using (clinical) information available at baseline, based on the Cox model\u003c\/li\u003e\n\u003cli\u003e \u003cem\u003ePart II\u003c\/em\u003e is about prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated\u003c\/li\u003e\n\u003cli\u003e \u003cem\u003ePart III\u003c\/em\u003e is dedicated to the use of time-dependent information in dynamic prediction\u003c\/li\u003e\n\u003cli\u003e \u003cem\u003ePart IV\u003c\/em\u003e explores dynamic prediction models for survival data using genomic data\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cstrong\u003eDynamic Prediction in Clinical Survival Analysis\u003c\/strong\u003e summarizes cutting-edge research on the dynamic use of predictive models with traditional and new approaches. Aimed at applied statisticians who actively analyze clinical data in collaboration with clinicians, the analyses of the different data sets throughout the book demonstrate how predictive models can be obtained from proper data sets. \u003c\/p\u003e","brand":"Taylor \u0026 Francis Inc","offers":[{"title":"Default Title","offer_id":54262218260824,"sku":"9781439835333","price":210.8,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0278\/1295\/4195\/files\/9781439835333_e24120cf-e3ee-47f4-894a-2fe3ef4bb970.jpg?v=1768467615","url":"https:\/\/agendabookshop.com\/products\/dynamic-prediction-in-clinical-survival-analysis-1","provider":"Agenda Bookshop","version":"1.0","type":"link"}