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

Longitudinal Research02:20

Longitudinal Research

12.8K
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...
12.8K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

2.8K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
2.8K
Longitudinal Studies01:26

Longitudinal Studies

294
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...
294
Censoring Survival Data01:09

Censoring Survival Data

287
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
287
Outliers and Influential Points01:08

Outliers and Influential Points

4.9K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.9K
Random Error01:04

Random Error

4.5K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
4.5K

You might also read

Related Articles

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

Sort by
Same author

Trajectory of irritability in children and youth in Ontario, Canada, throughout the COVID-19 pandemic.

JCPP advances·2026
Same author

Pediatric Reference and Optimal Curves for Hemoglobin.

JAMA network open·2026
Same author

Consumption of Fructose-Containing Food and Beverage Sources in Childhood Through to Adulthood and Risk of Hypertension: A Prospective Cohort Study.

Circulation·2026
Same author

Changes in obesity and waist circumference in children and parents during the COVID-19 pandemic.

Scientific reports·2026
Same author

What Outcome Measurement Instruments Are Used to Measure Caregiver Infant-Feeding Practices and the Feeding Environment: A Scoping Review.

Obesity reviews : an official journal of the International Association for the Study of Obesity·2026
Same author

Ferritin Reference Curves and Optimal Curves in Preadolescent Children.

JAMA network open·2026

Related Experiment Video

Updated: Oct 24, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K

Quantifying the extent of visit irregularity in longitudinal data.

Armend Lokku1,2, Catherine S Birken3,4,5,6, Jonathon L Maguire7,8,9,10

  • 1Child Health Evaluative Sciences, Hospital for Sick Children, Toronto, ON, Canada.

The International Journal of Biostatistics
|August 15, 2021
PubMed
Summary

Visit timing in longitudinal studies can bias results. We developed an Area Under the Curve (AUC) method to quantify visit irregularity, ensuring accurate data analysis and reducing bias in observational studies.

Keywords:
irregular visitslongitudinal datasummary measuresvisit intensityvisit process

More Related Videos

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.4K
Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
09:12

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

Published on: March 17, 2019

9.6K

Related Experiment Videos

Last Updated: Oct 24, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.4K
Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
09:12

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

Published on: March 17, 2019

9.6K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Observational Studies

Background:

  • Visit schedules in longitudinal studies can be irregular.
  • Ignoring visit timing can introduce bias into study outcomes.
  • Determining visit regularity is crucial for appropriate statistical analysis.

Purpose of the Study:

  • To propose a novel method for assessing visit irregularity in longitudinal data.
  • To quantify the extent to which study visits resemble regular repeated measures.
  • To provide a tool for improving the quality of statistical analyses in observational research.

Main Methods:

  • Proposed plotting mean proportions of individuals with 0 vs. >1 visit per bin.
  • Utilized the Area Under the Curve (AUC) as a quantitative measure of visit irregularity.
  • Validated the method through simulations and application to the TARGet Kids! study.

Main Results:

  • The AUC effectively quantifies visit irregularity.
  • AUC increases with greater visit irregularity.
  • AUC is invariant to sample size and number of scheduled visits, confirming its robustness.

Conclusions:

  • The AUC is a reliable single score for assessing visit irregularity.
  • This method helps determine if visits can be treated as repeated measures or irregular data.
  • Using AUC improves analytic approach selection and minimizes bias in longitudinal studies.