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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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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...
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Cluster Sampling Method01:20

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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.
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Longitudinal Research02:20

Longitudinal Research

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

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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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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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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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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Bayesian consensus clustering for multivariate longitudinal data.

Zihang Lu1, Wendy Lou2

  • 1Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada.

Statistics in Medicine
|October 21, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian consensus clustering (BCC) model for analyzing patient data over time. The model effectively identifies patient subtypes by clustering multiple longitudinal markers, offering deeper biological insights.

Keywords:
Bayesian consensus clusteringdisease clusteringintegrative clusteringmixture modelmultivariate longitudinal data

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Longitudinal Data Analysis

Background:

  • Clinical and epidemiological studies increasingly focus on patient heterogeneity using longitudinal data.
  • Clustering multiple longitudinal markers offers richer insights than single-marker approaches.

Purpose of the Study:

  • To propose a novel Bayesian consensus clustering (BCC) model for multivariate longitudinal data.
  • To identify patient subtypes based on co-existing longitudinal patterns.

Main Methods:

  • Developed a Bayesian consensus clustering (BCC) model for multivariate longitudinal data.
  • Employed a Gibbs sampling algorithm to estimate model parameters.
  • Applied the BCC model to primary biliary cirrhosis patient data.

Main Results:

  • The proposed BCC model successfully identified patient subtypes in the primary biliary cirrhosis study.
  • Simulation studies demonstrated the model's effectiveness compared to existing methods.
  • The model revealed co-existing longitudinal patterns for deeper biological insight.

Conclusions:

  • The Bayesian consensus clustering (BCC) model is a valuable tool for clustering multivariate longitudinal data.
  • This approach enhances the identification of patient subtypes and their prognostic associations.