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.
Table of Contents
Part I. Design Process
Chapter One Basics of Biomedical and Clinical Research
Chapter Two Research Design: Experimental & Non-experimental Design
Chapter Three Population, Sample, ,Biostatistical Reasoning & Probability
Part II. Biostatistical Modeling
Chapter Four Statistical Consideration in Clinical Research
Chapter Five Sample Size and Power Estimations
Chapter Six Single Sample Statistical Inference
Chapter Seven Two Independent Samples Statistical Inference
Chapter Eight Statistical Inference in Three or More Samples
Chapter Nine Statistical Inference Involving Relationships
Chapter Ten Special Topics in Modern Evidence Discovery
Laurens Holmes Jr. was trained in internal medicine, specializing in immunology and infectious diseases prior to his expertise in epidemiology (cancer)-with- biostatistics (survival analysis). Over the past two decades, Dr. Holmes had been working in cancer epidemiology, control & prevention. His involvement in biostatistical modeling of health research data includes signal amplification and stratification in risk modelling, evidence discovery through effect size and confidence interval (not p value) and evidence-based clinical and translational research through Quantitative Evidence Synthesis (QES).