Handling missing data in research

Q2 Medicine

Perspectives in Clinical Research Pub Date : 2024-04-01 DOI:10.4103/picr.picr_38_24

P. Ranganathan, Sally Hunsberger

引用次数: 0

Abstract

Missing data are an inevitable part of research and lead to a decrease in the size of the analyzable population, and biased and imprecise estimates. In this article, we discuss the types of missing data, methods to handle missing data and suggest ways in which missing data can be minimized.

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处理研究中的缺失数据

缺失数据是研究工作中不可避免的一部分，它会导致可分析人口数量的减少以及估计值的偏差和不精确。本文将讨论缺失数据的类型、处理缺失数据的方法，并提出尽量减少缺失数据的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Perspectives in Clinical Research Medicine-Medicine (all)

CiteScore

2.90

自引率

0.00%

发文量

审稿时长

36 weeks

期刊介绍： This peer review quarterly journal is positioned to build a learning clinical research community in India. This scientific journal will have a broad coverage of topics across clinical research disciplines including clinical research methodology, research ethics, clinical data management, training, data management, biostatistics, regulatory and will include original articles, reviews, news and views, perspectives, and other interesting sections. PICR will offer all clinical research stakeholders in India – academicians, ethics committees, regulators, and industry professionals -a forum for exchange of ideas, information and opinions.