Category 4: Standards for Preventing and Handling Missing Data
Learning Objectives
Cognitive: Recognize when and how missing data occur; Describe the impact of missing data on the validity and power of PCOR; Outline ways to prevent and monitor missing data during the design and conduct of PCOR.
Attitudinal: Value the importance of developing methods to prevent and monitor missing data.
Skills: Select appropriate statistical methods to deal with missing data that properly account for statistical uncertainty due to missingness; Apply sensitivity of inferences to missing data methods and assumptions about missing data mechanisms; Design data collection tools that record all reasons for missing data and account for all patients.
Learning Modules
This category contains the curriculum Introduction and six main modules. Learn about the instructors for this curriculum.
- Introductory Lecture: Prepared and presented by Jodi Segal, MD, MPH
- Learning Modules: Prepared and presented by Dan Scharfstein, ScD, and Tianjing Li, MD, PhD
Module 1: Introduction
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Module 2: What Are Missing Data?
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Module 3: Methods to Prevent and Monitor Missing Data
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Module 4: Record and Report Missing Data
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Module 5: Describe Statistical Methods to Handle Missing Data (Advanced)
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Module 6: Statistical Methods to Handle Missing Data and Methods of Examining Sensitivity of Inferences to Missing Data Methods and Assumptions (Advanced)
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Category 4 Self-Assessment
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Posted: February 2016
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