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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...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...

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Related Experiment Video

Updated: Jun 4, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Prediction based classification for longitudinal biomarkers.

A S Foulkes1, L Azzoni, X Li

  • 1Division of Biostatistics, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA USA.

The Annals of Applied Statistics
|January 29, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new prediction model to assess changes in CD4 count for individuals with HIV on antiretroviral therapy (ART). The model accurately predicts CD4 count changes, aiding in resource allocation for essential laboratory testing.

Related Experiment Videos

Last Updated: Jun 4, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

  • Immunology
  • Virology
  • Biostatistics

Background:

  • Monitoring CD4 count is crucial for HIV management during antiretroviral therapy (ART).
  • Limited resources in some settings necessitate efficient CD4 count testing strategies.
  • Identifying correlates of CD4 count change is essential for disease monitoring.

Purpose of the Study:

  • To develop and validate a prediction-based classification approach for assessing CD4 count change over time in HIV-infected individuals on ART.
  • To identify reliable correlates of CD4 count change to optimize laboratory resource allocation.

Main Methods:

  • A two-stage prediction-based classification approach was developed, incorporating modeling and classification based on clinical thresholds.
  • The method extends existing receiver operating characteristic curve analytical methods for continuous outcomes.
  • The algorithm was trained on a cohort of 270 HIV-1 infected individuals and validated on a separate cohort of 72 individuals.

Main Results:

  • The developed prediction algorithm achieved a positive predictive value greater than 98% for CD4 count change in an independent test sample.
  • The approach demonstrated high accuracy in predicting CD4 count trajectories.
  • Validation on a separate cohort confirmed the robustness of the prediction model.

Conclusions:

  • The proposed prediction-based classification method is a valuable tool for monitoring CD4 count changes in HIV-infected individuals on ART.
  • This approach can help prioritize laboratory resources for CD4 testing, particularly in resource-limited settings.
  • Accurate prediction of CD4 count dynamics can improve patient management and treatment strategies.