Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Longitudinal Studies01:26

Longitudinal Studies

91
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...
91
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

98
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
98
Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

111
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
111
Longitudinal Research02:20

Longitudinal Research

11.8K
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...
11.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

AD-GPT: large language models in Alzheimer's disease.

BMC medical informatics and decision making·2026
Same author

LTF-MSPCNet: A synergistic approach combining attention mechanisms and local texture features for oil spill segmentation in SAR images.

Marine pollution bulletin·2026
Same author

Large language models for bioinformatics.

Quantitative biology (Beijing, China)·2026
Same author

Spatiotemporal delivery of multifunctional nanozymes for neuroinflammation alleviation via autophagy modulation in spinal cord injury.

Materials today. Bio·2026
Same author

Bayesian monotone single-index quantile regression model with bounded response and misaligned functional covariates.

Biometrics·2025
Same author

VU0360172 exerts anti-inflammatory effect on germinal matrix hemorrhage in neonatal rats via the mGluR5/PI3Kγ/PPARγ pathway.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association·2025

Related Experiment Video

Updated: May 13, 2025

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage
06:46

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

Published on: August 4, 2018

12.1K

Single-Index Measurement Error Jump Regression Model in Alzheimer's Disease Studies.

Yan-Yong Zhao1, Kaizhou Lei2, Yuan Liu1,3

  • 1School of Statistics and Data Science, Nanjing Audit University, Nanjing, China.

Statistics in Medicine
|April 14, 2025
PubMed
Summary

This study introduces a new statistical model to analyze Alzheimer's disease (AD) risk factors affecting brain function. The model addresses complex data issues, revealing important jump patterns in patient neurocognitive performance.

Keywords:
Alzheimer's diseaseclustering‐based estimationjump discontinuitiesmeasurement errorsingle‐index model

More Related Videos

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

14.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

910

Related Experiment Videos

Last Updated: May 13, 2025

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage
06:46

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

Published on: August 4, 2018

12.1K
Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
09:38

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

14.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

910

Area of Science:

  • Neuroscience
  • Biostatistics
  • Medical Imaging

Background:

  • Alzheimer's disease (AD) is a leading cause of dementia in older adults.
  • Understanding risk factors impacting neurocognitive performance is vital for preventative strategies.
  • Existing models struggle with data exhibiting jump discontinuities and measurement errors.

Purpose of the Study:

  • To propose a novel statistical model for analyzing neurocognitive performance in Alzheimer's disease.
  • To address challenges posed by jump discontinuities and measurement errors in covariates.
  • To investigate the relationship between neurocognitive scores and various risk factors in AD patients.

Main Methods:

  • Development of a single-index measurement error jump regression model (SMEJRM).
  • Application of the model to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
  • Establishment of estimation procedures and asymptotic results.
  • Evaluation through simulation studies and real-data application.

Main Results:

  • The proposed SMEJRM effectively handles both jump discontinuities and measurement errors in covariates.
  • Simulation studies demonstrate the model's robust finite sample performance.
  • Real-world application confirmed the existence of jump discontinuities in AD patient data.

Conclusions:

  • The SMEJRM offers a powerful tool for analyzing complex relationships in Alzheimer's disease research.
  • The findings highlight the importance of considering non-linear patterns and data imperfections in neurocognitive studies.
  • This approach enhances the understanding of risk factors influencing neurocognitive decline in AD.