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Statistical Failings That Keep Us in the Dark & Practical Statistics

D Schlesinger
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J Sloan

W Sensakovic

D Schlesinger1*, J Sloan2*, W Sensakovic3*, (1) University of Virginia Health Systems, Charlottesville, VA, (2) Mayo Clinic, Rochester, MN, (3) Florida Hospital, Orlando, FL


4:30 PM : Not everything you read is true - D Schlesinger, Presenting Author
5:00 PM : Statistical failings that keep us all in the dark - J Sloan, Presenting Author
5:30 PM : Best practices for statistics in your own projects - W Sensakovic, Presenting Author

MO-F-201-0 (Monday, July 31, 2017) 4:30 PM - 6:00 PM Room: 201

Evidence is growing to suggest that a large number published clinical results cannot be replicated. At the same time, the number of published clinical papers is steadily increasing and most base their conclusions on evidence provided by formal statistical tests. Medical physicists have a critical need to understand and be able to interpret the methods and results of these studies in order to judge their scientific quality and relevance. However many medical physicists have minimal or no training in the sort of practical statistical methods commonly found in clinical studies. They may therefore have an inadequate ability to detect statistical errors and limitations that are an unfortunately frequent occurrence in clinical papers.

In this session we will use published examples to demonstrate common statistical errors frequently encountered in peer-reviewed literature, distinctive symptoms that can help detect these errors, and explanations for how they might have been corrected. In the process, we will explain some of the basic concepts of inferential statistics. Some specific case studies we will cover will include misunderstanding the meaning of p-values, misinterpreting statistical power, conflating statistical vs clinical significance, and faulty survival analysis.

Learning Objectives:
1. Learn about the frequency of statistical problems in published studies
2. Identify common signs and symptoms of potential problems in various types of statistical tests
3. Learn methods for correctly implementing statistical analyses of the type commonly found in clinical publications


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