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Related Concept Videos

Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Multiple imputation by chained equations: what is it and how does it work?

Melissa J Azur1, Elizabeth A Stuart, Constantine Frangakis

  • 1Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. mazur@mathematica-mpr.com

International Journal of Methods in Psychiatric Research
|April 19, 2011
PubMed
Summary
This summary is machine-generated.

Multivariate imputation by chained equations (MICE) offers a robust way to handle missing data in research. This guide introduces MICE methods and software, addressing practical challenges for psychiatric researchers.

Keywords:
analyzemissing datamultiple imputation

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Area of Science:

  • Psychiatric research
  • Statistical methodology
  • Data analysis

Background:

  • Missing data is a common challenge in psychiatric research.
  • Multivariate imputation by chained equations (MICE) is a principled statistical method for addressing missing data.
  • Existing resources for implementing MICE in psychiatric research are limited.

Purpose of the Study:

  • To provide an introduction to the Multivariate Imputation by Chained Equations (MICE) method.
  • To focus on the practical aspects and challenges of implementing MICE.
  • To review available software for MICE and analysis of multiply imputed data.

Main Methods:

  • The paper introduces the Multivariate Imputation by Chained Equations (MICE) technique.
  • It discusses practical considerations and potential challenges encountered during MICE implementation.
  • A review of software tools for performing MICE and analyzing the resulting imputed datasets is included.

Main Results:

  • MICE is presented as a valuable method for handling missing data in complex datasets.
  • Practical guidance and discussion of challenges aim to facilitate MICE adoption.
  • An overview of current software options supports researchers in applying MICE.

Conclusions:

  • MICE is an accessible and effective method for addressing missing data in psychiatric research.
  • This paper serves as a practical guide for researchers new to MICE.
  • Availability of MICE software encourages its wider application in the field.