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 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...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Observational Studies01:11

Observational Studies

Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One example of...
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...

You might also read

Related Articles

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

Sort by
Same author

Comparing Real and Virtual Nature Exposure on Cognition, Well-Being, and Brain Activity in Adults With and Without Attention-Deficit/Hyperactivity Disorder: Protocol for a Randomized Experimental Study.

JMIR research protocols·2026
Same author

Prevalence and moderators of personality disorders in adults with ADHD: A meta-analysis.

Psychiatry research·2026
Same author

"It has tentacles into every single aspect of me" a qualitative evidence synthesis of the lived experiences and perceptions of ADHD youth.

European child & adolescent psychiatry·2026
Same author

Depressive symptoms as a risk factor for postoperative delirium in older adults: A systematic review and meta-analysis.

Journal of affective disorders·2026
Same author

ADHD symptom manifestation in adulthood: moving beyond conceptualisations of inattention and hyperactivity/impulsivity.

Irish journal of psychological medicine·2026
Same author

A systematic review of transcranial direct current stimulation (tDCS) for adults with attention deficit/hyperactivity disorder (ADHD).

Journal of psychiatric research·2026

Related Experiment Video

Updated: Jun 25, 2026

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

Statistical methods for analysing longitudinal data in delirium studies.

Dimitrios Adamis1

  • 1Research and Academic Institute of Athens, Athens, Greece. dimaadamis@yahoo.com

International Review of Psychiatry (Abingdon, England)
|February 17, 2009
PubMed
Summary
This summary is machine-generated.

Analyzing longitudinal data in delirium research is challenging due to time-correlated variables. This review explores statistical methods like mixed-effects models to improve understanding of delirium

Related Experiment Videos

Last Updated: Jun 25, 2026

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

Area of Science:

  • Gerontology
  • Psychiatry
  • Biostatistics

Background:

  • Delirium is an acute, fluctuating syndrome with potential for prolonged disturbances.
  • Longitudinal studies are ideal for capturing delirium's natural course but face analytical challenges.
  • Correlated variables over time in longitudinal data limit the application of traditional analysis methods in delirium research.

Purpose of the Study:

  • To review and compare statistical methods for analyzing longitudinal data in delirium research.
  • To highlight the utility of advanced modeling techniques for binary outcomes (delirium/no delirium).

Main Methods:

  • Overview of traditional approaches and complex data modeling techniques.
  • Review of survival analysis, structural equation modeling, and path analysis.
  • Detailed examination of mixed-effects models and generalized estimating equations for binary outcomes, including correlation and missing data handling.

Main Results:

  • Contrasting approaches of mixed-effects models and generalized estimating equations are detailed.
  • Emphasis on parameter interpretation, correlation accounting, and missing data management.
  • Information on available statistical software is provided.

Conclusions:

  • Advanced statistical methods can significantly enhance the analysis of longitudinal delirium studies.
  • Incorporating these methods will substantially advance delirium research and understanding.