David Culliford

Applied Statistical Considerations for Clinical Researchers (eBook, PDF)

• Format: PDF

Produktbeschreibung

This essential book details intermediate-level statistical methods and frameworks for the clinician and medical researcher with an elementary grasp of health statistics and focuses on selecting the appropriate statistical method for many scenarios. Detailed evaluation of various methodologies familiarizes readers with the available techniques and equips them with the tools to select the best from a range of options. The inclusion of a hypothetical case study between a clinician and statistician charting the conception of the research idea through to results dissemination enables the reader to understand how to apply the concepts covered into their day-to-day clinical practice.

Applied Statistical Considerations for Clinical Researchers focuses on how clinicians can approach statistical issues when confronted with a medical research problem by considering the data structure, how this relates to their study's aims and any potential knock-on effects relating to the evidence required to make correct clinical decisions. It covers the application of intermediate-level techniques in health statistics making it an ideal resource for the clinician seeking an up-to-date resource on the topic.

Produktdetails

• Verlag: Springer International Publishing
• Erscheinungstermin: 18. November 2021
• Englisch
• ISBN-13: 9783030874100
• Artikelnr.: 62947517

Autorenporträt

David Culliford is a Principal Medical Statistician within the School of Health Sciences at the University of Southampton. David works as lead statistical consultant for the NIHR Applied Research Collaboration (ARC) Wessex, providing statistical consultancy services to all its researchers. His research uses large electronic health databases with a complex structure. His methodological interests include survival analysis, with a particular focus on methods which deal with missing data.

Inhaltsangabe

Introduction.- Preliminaries.- Design.- Planning.- Data Acquisition.- Data Manipulation Analysis.- Inferencesty.- Dissemination.- A Case Study.- Conclusions.

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Introduction.- Preliminaries.- Design.- Planning.- Data Acquisition.- Data Manipulation Analysis.- Inferencesty.- Dissemination.- A Case Study.- Conclusions.