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Titlebook: Advances in Statistical Methods for the Health Sciences; Applications to Canc Jean-Louis Auget,N. Balakrishnan,Geert Molenberghs Book 2007

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發(fā)表于 2025-3-21 17:49:30 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Advances in Statistical Methods for the Health Sciences
期刊簡(jiǎn)稱Applications to Canc
影響因子2023Jean-Louis Auget,N. Balakrishnan,Geert Molenberghs
視頻videohttp://file.papertrans.cn/150/149864/149864.mp4
發(fā)行地址Several active and distinguished researchers are contributors to the volume.Recent developments in a broad range of statistical methodoligies for the health sciences are presented.Applications to mode
學(xué)科分類Statistics for Industry and Technology
圖書封面Titlebook: Advances in Statistical Methods for the Health Sciences; Applications to Canc Jean-Louis Auget,N. Balakrishnan,Geert Molenberghs Book 2007
影響因子.Statistical methods have become an increasingly important and integral part of research in the health sciences. Many sophisticated methodologies have been developed for specific applications and problems. This self-contained volume, an outgrowth of an "International Conference on Statistics in Health Sciences," covers a wide range of topics pertaining to new statistical methods in the health sciences....The chapters, written by leading experts in their respective fields, are thematically divided into the following areas: prognostic studies and general epidemiology,?pharmacovigilance,?quality of life,?survival analysis,?clustering,?safety and efficacy assessment,?clinical design,?models for the environment,?genomic analysis, and?animal health...?..This comprehensive volume will serve the health science community as well as practitioners, researchers, and graduate students in applied probability, statistics, and biostatistics...?.
Pindex Book 2007
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N. Kalaichelvi,K. S. Easwarakumare discussed in the framework of regression models and classification and regression trees (CART). It is shown that the choice of one specific statistical method has a strong influence on the results and, therefore, on the interpretation of a prognostic marker. Within regression models we compare the
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S. Dhruv Shindhe,A. G. J. FaheemaUnfortunately, within any given disease area, the wealth of conflicting and heterogeneous evidence makes it difficult for the clinician to ascertain the overall evidence about specific markers and how to use them in practice. The application of formal methods (e.g., a systematic review and meta-anal
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https://doi.org/10.1007/978-3-031-39811-7rence, the first few occurrences, or an elevated rate to trigger a formal monitoring plan to identify whether that event occurs more frequently in the treated than in the control arm. The methods can apply either to rare events or to common events not expected to occur at an elevated rate in the tre
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S. Shrinithi,R. Lavanya,Devi Vijayanthe spontaneous reporting, by health professionals, of events that are supposed to be adverse effects of marketed drugs. SRS supply huge databases, the human-based exploitation of which cannot be exhaustive. Automated signal generation methods have been proposed in the literature but no consensus ex
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Wu-Sheng Wang,Zizun Li,Anmin TangQoL is usually evaluated using self-assessment questionnaires and responses to the items are combined into scores assumed to be normally distributed (which is rarely the case). An alternative is to use item response theory (IRT) models such as the partial credit model (PCM) for polytomous items whic
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https://doi.org/10.1007/978-3-642-84703-5vival process that takes into account the functional and nutritional evolution of the patients over time. We deal with both survival data and repeated measures, but the usual statistical methods for the joint analysis of longitudinal and survival data are not appropriate in this case. As an alternat
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