A state-of-the-art update of recent computational and methodological developments in medical and pharmaceutical statistics, Computational Methods in Biomedical Research emphasizes the applicability of techniques and medical and biopharmaceutical applications. The book presents a concise and critical review of newer and older methodologies and computational schemes, providing specific guidelines and recommendations. It also contains an extensive bibliography at the end of each chapter. This resource meets the needs of applied biostatisticians and researchers in public health or medical schools,…mehr
A state-of-the-art update of recent computational and methodological developments in medical and pharmaceutical statistics, Computational Methods in Biomedical Research emphasizes the applicability of techniques and medical and biopharmaceutical applications. The book presents a concise and critical review of newer and older methodologies and computational schemes, providing specific guidelines and recommendations. It also contains an extensive bibliography at the end of each chapter. This resource meets the needs of applied biostatisticians and researchers in public health or medical schools, hospitals, pharmaceutical industry, government, and other research facilities.
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Inhaltsangabe
Preface. Microarray Data Analysis. Machine Learning Techniques for Bioinformatics: Fundamentals and Applications. Machine Learning Methods for Cancer Diagnosis and Prognostication. Protein Profiling for Disease Proteomics with Mass Spectrometry: Computational Challenges. Predicting US Cancer Mortality Counts Using State Space Models. Analyzing Multiple Failure Time Data Using SAS® Software. Mixed-Effects Models for Longitudinal Virologic and Immunologic HIV Data. Bayesian Computational Methods in Biomedical Research. Sequential Monitoring of Randomization Tests. Proportional Hazards Mixed-Effects Models and Applications. Classification Rules for Repeated Measures Data from Biomedical Research. Estimation Methods for Analyzing Longitudinal Data Occurring in Biomedical Research. Index.
Preface. Microarray Data Analysis. Machine Learning Techniques for Bioinformatics: Fundamentals and Applications. Machine Learning Methods for Cancer Diagnosis and Prognostication. Protein Profiling for Disease Proteomics with Mass Spectrometry: Computational Challenges. Predicting US Cancer Mortality Counts Using State Space Models. Analyzing Multiple Failure Time Data Using SAS® Software. Mixed-Effects Models for Longitudinal Virologic and Immunologic HIV Data. Bayesian Computational Methods in Biomedical Research. Sequential Monitoring of Randomization Tests. Proportional Hazards Mixed-Effects Models and Applications. Classification Rules for Repeated Measures Data from Biomedical Research. Estimation Methods for Analyzing Longitudinal Data Occurring in Biomedical Research. Index.
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