Sec.B-Ch.2-Subsec.4：Handling Missing Data in Clinical Research Using R
Causes, Classification, Impact, and Practical Detection and Visualization Methods in Medical Data Analysis
In clinical research, missing data are frequently encountered due to reasons such as loss to follow-up, non-response, or unclear records. This article explores the causes, classifications, impacts, and solutions for missing data in medical research. Combined with practical applications in R, it provides a comprehensive guide for medical researchers.
I. Understanding Missing Data
In many longitudinal medical studies, data collection takes a long time. During this period, participants may recover from disease, relocate, or experience other life changes. Therefore, missing data are often unavoidable.
Missing data not only reduce the statistical power of medical research but may also introduce bias into the study results.
Incomplete data caused by loss to follow-up, refusal to participate, or data entry errors during data collection are referred to as missing data.
1. Operational Causes of Missing Data
In medical research, missing data can arise from various sources, which can generally be categorized as follows.
