Applied Epidemiologic Principles and Concepts: Clinicians' Guide to Study Design and Conduct

This book provides practical knowledge to clinicians and biomedical researchers 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 presents the extreme complexity of epidemiologic research in a concise manner that will address the issue of confounders, thus allowing for more valid inferences and yielding results that are more reliable and accurate.

About the Author

Professor Laurens Holmes Jr. was trained in internal medicine, specializing in immunology and infectious diseases prior to his expertise in epidemiology-with- biostatistics. He is an advocate of reality in modeling of epidemiologic, clinical and biomedical research data, consequential epidemiology, as well as explanatory ecologic model in understanding the complex etiologies of health and disease. Over the past two decades, Dr. Holmes had been working in cancer epidemiology, control & prevention with substantial publication on hormonally-related malignancies mainly prostate, thyroid, cervical, and testicular. Other areas where his focus had been during the last fifteen years include pediatric malignancy namely ALL, AML, lymphoma, brain and CNS. His involvement in chronic disease epidemiology, control and prevention includes signal amplification and stratification in risk modeling and health disparities in hypertension, and diabetes mellitus with large legacy (preexisting US National Health Statistics Center) data. Professor Holmes is a pioneer of health disparities epidemiology where he presents on disentangling race in epidemiologic studies by re-considering the biologic,bio-molecular, genetic, epigenetic determinants of disease incidence, prevalence, morbidity, prognosis, severity and mortality.