Applied Biostatistical Principles and Concepts: Clinicians' Guide to Data Analysis and Interpretation
Author:   Laurens Holmes, Jr. (Nemours Healthcare System, Wilmington, Delaware, USA)
Publisher:   Taylor & Francis Ltd
ISBN:  

9780367560072


Pages:   288
Publication Date:   31 March 2021
Format:   Paperback
Availability:   In Print   Availability explained
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Applied Biostatistical Principles and Concepts: Clinicians' Guide to Data Analysis and Interpretation


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Author:   Laurens Holmes, Jr. (Nemours Healthcare System, Wilmington, Delaware, USA)
Publisher:   Taylor & Francis Ltd
Imprint:   Routledge
Weight:   0.500kg
ISBN:  

9780367560072


ISBN 10:   0367560070
Pages:   288
Publication Date:   31 March 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

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

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Author Information

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).

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