What question did this study set out to answer?

The aim is to explore statistical methods for addressing missing data in cardiovascular studies and highlight their advantages over conventional methods.

May 18, 2026

Missing Data Essentials Part 2: Statistical Approaches to Missing Data in Cardiovascular Studies

Key Points

The aim is to explore statistical methods for addressing missing data in cardiovascular studies and highlight their advantages over conventional methods.
Presents principled methods including multiple imputation and full information maximum likelihood estimation.
Provides worked examples to illustrate the application of each approach to missing variables.
Discusses limitations of conventional methods and their potential to introduce bias.
Principled methods mitigate bias more effectively compared to conventional approaches.
Multiple imputation and full information maximum likelihood provide robust frameworks for data handling.
Illustrative examples show successful application of advanced statistical techniques.

Abstract

Missing data are common in cardiovascular nursing and allied health research. Although conventional methods of handling missing data may appear straightforward, they have significant limitations and can introduce bias. In contrast, principled methods, including multiple imputation with and without chained equations and full information maximum likelihood estimation, offer more robust ways to mitigate bias and make full use of available information. These methods of handling missingness can be applied to missing independent and dependent variables, and this methods paper provides worked examples to illustrate each approach.

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