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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

171
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...
171
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

201
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
88
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

208
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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Longitudinal Research02:20

Longitudinal Research

12.0K
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...
12.0K
Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K

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Updated: Jul 12, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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clusterMLD: An Efficient Hierarchical Clustering Method for Multivariate Longitudinal Data.

Junyi Zhou1, Ying Zhang2, Wanzhu Tu1

  • 1Department of Biostatistics and Health Data Science, Indiana University.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|October 20, 2023
PubMed
Summary

This study introduces a new clustering method for longitudinal data, effectively grouping individuals based on their unique trajectories even with sparse, irregular measurements. The approach demonstrates superior accuracy and efficiency in cluster identification and classification.

Keywords:
B-splinesDissimilarity metricFunctional DataLongitudinal dataMultiple outcomes

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Area of Science:

  • Biostatistics
  • Data Science
  • Machine Learning

Background:

  • Clustering longitudinal data presents challenges due to sparse and irregular observations.
  • Existing methods struggle to accurately group individuals based on trajectory similarity.
  • Accurate clustering is crucial for understanding patient heterogeneity in clinical studies.

Purpose of the Study:

  • To develop a novel hierarchical agglomerative clustering method for longitudinal data.
  • To address the challenges of sparse and irregular measurements in trajectory analysis.
  • To provide a robust tool for clustering multivariate longitudinal and functional data.

Main Methods:

  • A hierarchical agglomerative clustering approach is proposed.
  • A novel dissimilarity metric quantifies the cost of merging trajectory groups.
  • B-splines are utilized to represent individual data trajectories.

Main Results:

  • The proposed method excels in determining the optimal number of clusters.
  • It demonstrates superior performance in correctly classifying individuals into clusters.
  • The method offers significant computational efficiency compared to existing approaches.

Conclusions:

  • The new clustering method is effective for both sparse/irregular and dense longitudinal data.
  • An R package is provided for practical implementation of the analysis.
  • The method's utility is demonstrated on real-world clinical datasets.