Applied Biostatistical Principles and Concepts: Clinicians' Guide to Data Analysis and Interpretation

Laurens Holmes, Jr.

© 2017 - Routledge
Published November 20, 2017
Reference - 288 Pages - 52 B/W Illustrations
ISBN 9781498741194 - CAT# K26727

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      • Provides rationale and assumption behind all statistical test
      • Presents reality in Statistical Modelling of clinical and biomedical research data
      • Illustrates "Big data" and Biostatistical Reasoning & Inference
      • Explains in detail most commonly used and abused parametric (t test) and non-parametric test (chi-square)
      • Delves into logistic/Binomial Regression Models – Binary Outcomes
      • Examines Time-to-Event Data - Survival Analysis & Count Data – Poisson Regression
      • Explains Panel/Longitudinal Data - cross-sectional and time series - Poolability
      • Discusses Clinical/Biologic relevance & Statistical Significance – p value as a function of sample size
      • Stresses parameter value over parameter precision



    The past three decades have witnessed modern advances in statistical modeling and evidence discovery in biomedical, clinical, and population-based research. With these advances come the challenges in accurate model stipulation and application of models in scientific evidence discovery

    Applied Biostatistical Principles and Concepts provides practical knowledge using biological and biochemical specimen/samples in order to understand health and disease processes at cellular, clinical, and population levels. Concepts and techniques provided will help researchers design and conduct studies, then translate data from bench to clinics in attempt to improve the health of patients and populations.

    This book is suitable for both clinicians and health or biological sciences students. It presents the reality in statistical modelling of health research data in a concise manner that will address the issue of "big data" type I error tolerance and probability value, effect size and confidence interval for precision, effect measure modification and interaction as well as confounders, thus allowing for more valid inferences and yielding results that are more reliable, valid and accurate.


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