Recent Advances in Biostatistics

Recent Advances in Biostatistics
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Format E-Bok
Filformat PDF
Utgivelsesår 2011
Forlag World Scientific Publishing Co
Språk Engelsk
ISBN 9789814329804
Sider 312
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Om Recent Advances in Biostatistics

This unique volume provides self-contained accounts of some recent trends in Biostatistics methodology and their applications. It includes state-of-the-art reviews and original contributions.The articles included in this volume are based on a careful selection of peer-reviewed papers, authored by eminent experts in the field, representing a well balanced mix of researchers from the academia, R&D sectors of government and the pharmaceutical industry.The book is also intended to give advanced graduate students and new researchers a scholarly overview of several research frontiers in biostatistics, which they can use to further advance the field through development of new techniques and results.Contents:False Discovery Rates:A New Adaptive Method to Control the False Discovery Rate (F Liu & S K Sarkar)Adaptive Multiple Testing Procedures Under Positive Dependence (W-G Guo et al.) A False Discovery Rate Procedure for Categorical Data (J F Heyse)Survival Analysis:Conditional Nelson-Aalen and Kaplan-Meier Estimators with the Müller–Wang Boundary Kernel (X-D Luo & W-Y Tsai)Regression Analysis in Failure Time Mixture Models with Change Points According to Thresholds of a Covariate (J-M Lee et al.)Modeling Survival Data Using the Piecewise Exponential Model with Random Time Grid (F N Demarqui et al.)Proportional Rate Models for Recurrent Time Event Data Under Dependent Censoring: A Comparative Study (L D A F Amorim et al.)Efficient Algorithms for Bayesian Binary Regression Model with Skew-Probit Link (R B A Farias & M D Branco)M-Estimation Methods in Heteroscedastic Nonlinear Regression Models (C Lim et al.)The Inverse Censoring Weighted Approach for Estimation of Survival Functions from Left and Right Censored Data (S Subramanian & P-X Zhang)Analysis and Design of Competing Risks Data in Clinical Research (H T Kimn)Related Topics: Genomics/Bioinformatics, Medical Imaging and Diagnosis, Clinical Trials:Comparative Genomic Analysis Using Information Theory (S N Fatakia et al.) Statistical Modeling for Data of Positron Emission Tomography in Depression (C Chang & R T Ogden)The Use of Latent Class Analysis in Medical Diagnosis (D Rindskopf)Subset Selection in Comparative Selection Trials (C-S Leu et al.)Readership: Advanced Graduate students; active researchers in universities, research labs in government and industry engaged in and concerned with modeling and data analysis in biostatistics; R&D managers and directors of biostatistics / public health research in government and industry.


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