Free Applying Quantitative Bias Analysis To Epidemiologic Data (Statistics For Biology And Health) Ebooks Online
Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside from listing them as limitations of their particular study. This listing is often accompanied by explanations for why the limitations should not pose much concern. On the supply side, methods for bias analysis receive little attention in most epidemiology curriculums, are often scattered throughout textbooks or absent from them altogether, and cannot be implemented easily using standard statistical computing software. Our objective in this text is to reduce these supply-side barriers, with the hope that demand for quantitative bias analysis will follow.
Series: Statistics for Biology and Health
Hardcover: 192 pages
Publisher: Springer; 2009 edition (May 12, 2009)
Language: English
ISBN-10: 0387879609
ISBN-13: 978-0387879604
Product Dimensions: 6.1 x 0.5 x 9.2 inches
Shipping Weight: 1 pounds (View shipping rates and policies)
Average Customer Review: Be the first to review this item
Best Sellers Rank: #1,203,861 in Books (See Top 100 in Books) #280 in Books > Computers & Technology > Computer Science > Computer Simulation #289 in Books > Textbooks > Medicine & Health Sciences > Research > Biostatistics #350 in Books > Medical Books > Nursing > Home & Community Health
Applying Quantitative Bias Analysis to Epidemiologic Data (Statistics for Biology and Health) Analytics: Data Science, Data Analysis and Predictive Analytics for Business (Algorithms, Business Intelligence, Statistical Analysis, Decision Analysis, Business Analytics, Data Mining, Big Data) Data Analytics: Practical Data Analysis and Statistical Guide to Transform and Evolve Any Business. Leveraging the Power of Data Analytics, Data ... (Hacking Freedom and Data Driven) (Volume 2) Data Analytics: What Every Business Must Know About Big Data And Data Science (Data Analytics for Business, Predictive Analysis, Big Data) Statistics and Data Analysis for Financial Engineering: with R examples (Springer Texts in Statistics) Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis Case Studies in Certified Quantitative Risk Management (CQRM): Applying Monte Carlo Risk Simulation, Strategic Real Options, Stochastic Forecasting, ... Business Intelligence, and Decision Modeling Handbook of Freshwater Fishery Biology, Volume 2: Life History Data on centrarchid Fishes of the United States and Canada (Handbook of Freshwater Fishery Biology) Head First Data Analysis: A learner's guide to big numbers, statistics, and good decisions Statistics and Data Analysis for Nursing Research (2nd Edition) Forecasting Product Liability Claims: Epidemiology and Modeling in the Manville Asbestos Case (Statistics for Biology and Health) Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine (Statistics for Biology and Health) Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health) Still Failing at Fairness: How Gender Bias Cheats Girls and Boys in School and What We Can Do About It Bias: A CBS Insider Exposes How the Media Distort the News Making Diversity Work: 7 Steps for Defeating Bias in the Workplace Breaking Through Bias: Communication Techniques for Women to Succeed at Work Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) RNA-seq Data Analysis: A Practical Approach (Chapman & Hall/CRC Mathematical and Computational Biology)
