The prevention and handling of the missing data

By Hyun Kang. Korean Journal of Anesthesiology, 2013

1,559 citations recorded by the source corpus.

Even in a well-designed and controlled study, missing data occurs in almost all research. Missing data can reduce the statistical power of a study and can produce biased estimates, leading to invalid conclusions. This manuscript reviews the problems and types of missing data, along with the techniques for handling missing data. The mechanisms by which missing data occurs are illustrated, and the methods for handling the missing data are discussed. The paper concludes with recommendations for the handling of missing data.

Subfields: Health Information Management, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6ff19b95d318efb4b66c8f09e108a77cd995b028

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Information Management

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research* (2020, background): “Missing data, common in all areas of research, can impact the sensitivity of the study and also lead to biased estimates, distorted power, and loss of information if the missing values are not random [54].”
Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review (2018, positive): “It is important to note that there are other factors that deserve attention, which arise even in “successful” trials, including (1) whether or not the appropriate outcome measure is chosen, particularly if it is a surrogate measure [27,48,68,120], (2) how missing data are handled, and whether values”
A survey on missing data in machine learning (2021, background): “Moreover, the seriousness of missing values depend in part on how much data is missing, the pattern of missing data, and the mechanism underlying the missingness of the data [4].”
Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020, background): “Missing data, common in all areas of research, can impact the sensitivity of the study and also lead to biased estimates, distorted power, and loss of information if the missing values are not random [54].”
Efficient machine learning models for prediction of concrete strengths (2021, methodology): “275 In this study, the mean imputation which is a common method of imputing missing data 276 is utilised [46, 47].”