{"product_id":"probability-modeling-and-statistical-inference-in-cancer-screening-9781032513300","title":"Probability Modeling and Statistical Inference in Cancer Screening","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eCancer screening has been around for six decades, but there are still many unsolved problems. This book provides a concise account of the analysis of cancer screening data using probability models and statistical methods, with real data sets provided to help researchers and practitioners apply the methods. It develops statistical methods in the commonly used disease progressive model and provides solutions to practical problems. The book is primarily aimed at researchers and practitioners from biostatistics and cancer research and can be used as a one-semester textbook on cancer screening methodology. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 262 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 06 February 2024\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Taylor \u0026amp; Francis Ltd\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eCancer screening has been a vital practice for over six decades, yet numerous challenges remain unsolved. These challenges include estimating key parameters such as sensitivity, the time duration in the preclinical state (sojourn time), and the time duration in the disease-free state. Additionally, determining the distribution of lead time, the diagnosis time advanced by screening, evaluating the long-term outcomes of screening, including the probability of overdiagnosis among the screen-detected, determining the appropriate timing for the initial exam based on an individual's current age and risk tolerance, and planning subsequent exams based on their screening history, age, and risk tolerance require proper probability models and statistical methods. Addressing these complex problems necessitates the application of robust probability models and statistical techniques.\u003cbr\u003e\u003cbr\u003eThis book offers a comprehensive and concise overview of the analysis of cancer screening data, employing probability models and statistical methods. It provides real data sets to facilitate the learning process for cancer researchers and statisticians. The book develops statistical methods within the commonly used disease progressive model, offering solutions to practical problems and presenting open challenges. It serves as a valuable resource for researchers and practitioners in the fields of biostatistics and cancer research, with a prerequisite knowledge of calculus, probability, and statistical inference. The book can be effectively utilized as a one-semester textbook on cancer screening methodology for advanced graduate-level courses.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 689g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 234 x 156 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9781032513300\u003c\/p\u003e","brand":"Dongfeng Wu","offers":[{"title":"Hardback","offer_id":45200788979962,"sku":"9781032513300","price":99.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/1708108654838_book.jpg?v=1708250518","url":"https:\/\/shulphink.com\/products\/probability-modeling-and-statistical-inference-in-cancer-screening-9781032513300","provider":"Shulph Ink","version":"1.0","type":"link"}