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Related Concept Videos

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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)...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis.

Jos Twisk1, Michiel de Boer, Wieke de Vente

  • 1Department of Epidemiology and Biostatistics, VU University Medical Centre, de Boelelaan 1118, Amsterdam 1081 HV, The Netherlands. jwr.twisk@vumc.nl

Journal of Clinical Epidemiology
|June 25, 2013
PubMed
Summary

Multiple imputation is not necessary before mixed-model analysis for longitudinal data. Studies show mixed-model analysis results are similar with or without multiple imputation, and imputation can be unstable.

Keywords:
Longitudinal studiesMissing data mechanismsMissing data patternsMixed modelsMultiple imputationStatistical methods

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Growing interest in handling missing data in longitudinal studies.
  • Sophisticated techniques like multiple imputation are increasingly used.
  • Debate exists on the necessity of multiple imputation before mixed-model analysis.

Purpose of the Study:

  • Evaluate the necessity of multiple imputation before mixed-model analysis for longitudinal data.
  • Compare results of mixed-model analyses with and without multiple imputation.
  • Assess the impact of different missing data mechanisms on the comparison.

Main Methods:

  • Compared mixed-model analyses performed with and without multiple imputation.
  • Utilized four datasets with varying missing data mechanisms (MCAR, MAR, MNAR).
  • Analyzed relationships between a continuous outcome and dichotomous/continuous covariates.

Main Results:

  • Mixed-model analyses showed only slight differences with or without multiple imputation.
  • No approach (with or without imputation) consistently outperformed the other.
  • Multiple imputation results demonstrated instability when repeated 100 times.

Conclusions:

  • Multiple imputation is not a necessary step before performing mixed-model analysis on longitudinal data.
  • Mixed-model analysis can be reliably performed without prior multiple imputation.
  • The stability of multiple imputation results warrants careful consideration.