Clinical Epidemiology -

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Editeur : Springer Us, New York, N.Y.

Langue : Anglais

Parution : 01/04/2022

Format : Moyen, de 350g à 1kg

Nombre de pages : 664

Expédition : 1228

Dimensions : 25.4 x 17.8 x 3.6

ISBN : 1071611402

Résumé :

This third edition volume expands on the previous editions with updated chapters on longitudinal studies, randomized trials, evidence-based decisions making, and a new section on changing health-related behaviors. The chapters in this book are organized into six parts: Part One focuses on framing clinical research questions and choosing a suitable design; biases that may occur in clinical research; and the ethics associated with doing conducting research on humans. Parts Two through Four discuss designs, measurements, and analysis that pertain to evaluation of risk in longitudinal studies; assessment of therapy in controlled trials; and evaluation of diagnostic tests. Part Five presents methods used in various components of evidence-based decision-making; and Part Six highlights interventions focused on changing health-related behaviors. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of various types of bias, step-by-step, readily reproducible protocols for different research designs, and tips on troubleshooting and avoiding known pitfalls.

Cutting-edge and thorough, Clinical Epidemiology: Methods and Protocols, Third Edition is a valuable resource for clinicians and researchers who want to expand their works to humans and use their findings in the health system.

Sommaire:
On Framing the Research Question and Choosing the Appropriate Research Design.- Bias in Clinical Research.- Definitions of Bias in Clinical Research.- Research Ethics for Clinical Researchers.- Ethical, Legal, and Social Issues (ELSI) in Clinical Genetics Research.- Longitudinal Studies 1: Determinants of Risk.- Longitudinal Studies 2: Modeling Data using Multivariate Analysis.- Longitudinal Studies 3: Data Modeling using Standard Regression Models and Extensions.- Longitudinal Studies 4: Matching Strategies to Evaluate Risk.- Longitudinal Studies 5: Development of Risk Prediction Models for Patients with Chronic Disease.- Randomized Controlled Trials 1: Design.- Randomized Controlled Trials 2: Analysis.- Randomized Controlled Trials 3: Measurement and Analysis of Patient-Reported Outcomes.- Randomized Controlled Trials 4: Planning, Analysis, and Interpretation of Quality of Life Studies.- Randomized Controlled Trials 5: Biomarkers and Surrogates/Outcomes.- Randomized Controlled Trials 6: Determining the Sample Size and Power for Clinical Trials and Cohort Studies.- Randomized Controlled Trials 7: On Contamination and Estimating the Actual Treatment Effect.- Evaluation of Diagnostic Tests.- Genetic Epidemiology of Complex Phenotypes.- Qualitative Research in Clinical Epidemiology.- Evidence Based Decision Making 1: Critical Appraisal.- Evidence Based Decision Making 2: Systematic Review and Meta-Analysis.- Evidence-Based Decision Making 3: Health Technology Assessment.- Evidence-Based Decision Making 4: Clinical Practice Guidelines.- Evidence-Based Decision Making 5: Knowledge Translation and the Knowledge to Action Cycle.- Evidence-Based Decision Making 6: Administrative Databases as Secondary Data Source for Epidemiologic and Health Service Research.- Methods in Evidence-Based Decision Making 7: Health Economics in Clinical Research.- Methods in Evidence-Based Decision Making 8: A Primer for Researchers.- Changing Health-Related Behaviors 1: Patient-Oriented Research and Patient Engagement in Health Research.- Changing Health-Care Behaviors 2: On Improving the Value of Health Spending.- Changing Health-Related Behaviors 3: Lessons from Implementation Science.- Changing Health-Related Behaviors 4: Realizing Impact of Health Research through Knowledge Translation.- Changing Health-Related Behaviors 5: On Interventions to Change Physician Behaviors.- Changing Health-Related Behaviors 6: Analysis, Interpretation, and Application of Big Data.