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.
Part I. Design ProcessChapter One Basics of Biomedical and Clinical ResearchChapter Two Research Design: Experimental & Non-experimental DesignChapter Three Population, Sample, ,Biostatistical Reasoning & ProbabilityPart II. Biostatistical ModelingChapter Four Statistical Consideration in Clinical ResearchChapter Five Sample Size and Power EstimationsChapter Six Single Sample Statistical InferenceChapter Seven Two Independent Samples Statistical InferenceChapter Eight Statistical Inference in Three or More SamplesChapter Nine Statistical Inference Involving Relationships Chapter Ten Special Topics in Modern Evidence Discovery