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Related Concept Videos

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

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

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Related Experiment Video

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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

TWO-STAGE EMPIRICAL LIKELIHOOD FOR LONGITUDINAL NEUROIMAGING DATA.

Xiaoyan Shi1, Joseph G Ibrahim, Jeffrey Lieberman

  • 1University of North Carolina at Chapel Hill.

The Annals of Applied Statistics
|July 19, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method, TAETEL, for analyzing longitudinal neuroimaging data. TAETEL efficiently handles complex brain data, aiding research into neuropsychiatric disorders and brain development.

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Last Updated: May 31, 2026

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

  • Neuroimaging
  • Statistical Analysis
  • Neuroscience

Background:

  • Longitudinal imaging is crucial for understanding brain development and disorders.
  • Analyzing complex longitudinal neuroimaging data presents significant statistical challenges.
  • Existing methods may struggle with temporal correlations and spatial dependencies.

Purpose of the Study:

  • To develop a novel statistical method, the two-stage adjusted exponentially tilted empirical likelihood (TAETEL), for spatial analysis of longitudinal neuroimaging data.
  • To enable efficient analysis of longitudinal data without precise temporal correlation modeling.
  • To classify time-dependent covariate types and account for spatial dependence.

Main Methods:

  • Developed a two-stage adjusted exponentially tilted empirical likelihood (TAETEL) method.
  • Incorporated spatial analysis by weighting data from neighboring voxels/pixels.
  • Utilized simulation studies to evaluate the performance of TAETEL and related statistics.
  • Applied the method to analyze morphological changes in the hippocampus in schizophrenia patients.

Main Results:

  • The TAETEL method provides efficient analysis of longitudinal neuroimaging data.
  • The method effectively handles spatial dependence by integrating neighborhood voxel information.
  • Simulation studies confirmed the finite sample performance of the TAETEL statistics.
  • The application successfully detected differences in hippocampal morphology over time between schizophrenia patients and controls.

Conclusions:

  • TAETEL is a powerful statistical tool for analyzing longitudinal neuroimaging data, particularly in the context of neuropsychiatric disorders.
  • The method's ability to handle temporal and spatial correlations makes it valuable for brain development and disease research.
  • This approach facilitates the detection of subtle morphological changes in conditions like schizophrenia.