Methods and implications of addressing missing data in health-care research

Abstract

Missing data can introduce biases and affect the generalizability of research findings, undermining the scientific rigor of studies and impeding the development of evidence-based practices. To overcome these challenges, health-care providers and researchers must adopt robust strategies to identify, prevent, and handle missing data effectively. This includes implementing standardized data collection procedures, utilizing advanced statistical techniques to account for missingness, and embracing emerging technologies that facilitate comprehensive and timely data capture. By prioritizing the management of missing data, health-care professionals and researchers can enhance patient care, improve research quality, and ultimately contribute to better health outcomes for individuals and populations.

Keywords

• biases
• imputation method
• missing data