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 secondary analysis of our previous systematic review, which examined 58 studies to uncover influential factors for selecting optimal imputation methods. Second, we analyzed the implementation process, focusing on the properties of missing values and selecting appropriate imputation techniques while verifying the underlying assumptions according to the estimand framework from the ICH E9(R1) Guideline to ensure unbiased estimates and enhance the credibility of our findings.

Results: The findings from the secondary analysis suggest that the primary concepts of the developed conceptual framework directly influence the selection of appropriate imputation methods.

Conclusions: This integrated structure will enable researchers to select the most suitable imputation method based on the specific characteristics and conditions of the dataset under investigation. By employing the appropriate imputation method, the study aims to enhance the overall quality and trustworthiness of the analytical outcomes derived from the research dataset.