Conceptual framework as a guide to choose appropriate imputation method for missing values in a clinical structured dataset.

ABSTRACT:

Background

Missing data is a common challenge in structured datasets, and numerous methods are available for imputing these missing values. While all of these imputation methods address the issue of incomplete data, it is important to note that some methods perform better than others in terms of their effectiveness. A thorough data analysis can help a researcher identify a given dataset's most appropriate imputation approach, leading to more reliable analytical results. The primary objective of this study is to develop a conceptual framework that integrates various data imputation methods.

Methods

This study was conducted in two main steps. First, we defined the conceptual components and their interrelationships by identifying and categorizing primary concepts through a seco

SUBMITTER: Afkanpour M 

PROVIDER: S-EPMC11843774 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature