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

Orthogonal Trajectories01:26

Orthogonal Trajectories

71
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
71
Interpreting R Charts01:22

Interpreting R Charts

359
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
359
Drug Classes and Categories01:25

Drug Classes and Categories

3.1K
Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
3.1K
Antibody Structure and Classes01:25

Antibody Structure and Classes

9.2K
Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.
9.2K
Interpreting Run Charts01:25

Interpreting Run Charts

4.0K
Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
4.0K
Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

3.4K
An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
3.4K

You might also read

Related Articles

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

Sort by
Same author

Association between lifestyle and risk of early-onset cancer: Evidence from the European Prospective Investigation into Cancer and Nutrition and the UK Biobank.

European journal of cancer (Oxford, England : 1990)·2026
Same author

Body mass index, adjuvant chemotherapy, toxicity, and survival in non-metastatic colorectal cancer: an individual participant data meta-analysis (OCTOPUS).

British journal of cancer·2026
Same author

Priorities for medication management information resources for people with dementia and carers: a community-driven approach using a modified Delphi method.

Age and ageing·2026
Same author

Age at First Pregnancy, Adult Weight Gain and Postmenopausal Breast Cancer Risk: The PROCAS Study (United Kingdom).

International journal of cancer·2026
Same author

Sensitive Period Analysis of Adulthood BMI and Cancer Risk: An Individual Participant Data Meta-Analysis of Over 720,000 Participants in the ABACus 2 Consortium.

International journal of cancer·2026
Same author

Upper gastrointestinal cancer risk following bariatric surgery: meta-analysis.

BJS open·2026
Same journal

Determinants of high annual sickness absence in older workers: a prospective cohort study in England (Health and Employment After Fifty study).

BMJ open·2026
Same journal

Routine external aortic compression versus no aortic compression in elective caesarean delivery to reduce blood loss: study protocol of a randomised controlled trial.

BMJ open·2026
Same journal

Protocol for a systematic review of mental healthcare for Afghan refugees and asylum seekers in high-income countries.

BMJ open·2026
Same journal

Caregiving challenges among caregivers of patients with low-vision glaucoma: a qualitative exploration at the Presbyterian Hospital, Agogo, Ghana.

BMJ open·2026
Same journal

Teaching AI ethics in medical schools: a scoping review protocol on the ethical-technical balance in curricular frameworks.

BMJ open·2026
Same journal

Ophthalmic self-medication among adult ophthalmic patients attending a tertiary eye care centre in Northwest Ethiopia: a mixed-methods study assessing prevalence, associated factors and lived experience.

BMJ open·2026
See all related articles

Related Experiment Video

Updated: Feb 8, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K

Framework to construct and interpret latent class trajectory modelling.

Hannah Lennon1,2, Scott Kelly3, Matthew Sperrin2

  • 1Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

BMJ Open
|July 9, 2018
PubMed
Summary
This summary is machine-generated.

Latent class trajectory modelling (LCTM) simplifies complex populations into distinct patterns. This study introduces a systematic framework to identify a core, generalizable LCTM, enhancing epidemiological research reliability.

Keywords:
growth curvesgrowth mixture modelslatent class modelslifetime obesitytrajectories

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.7K
Studying Cell Rolling Trajectories on Asymmetric Receptor Patterns
04:24

Studying Cell Rolling Trajectories on Asymmetric Receptor Patterns

Published on: February 13, 2011

9.9K

Related Experiment Videos

Last Updated: Feb 8, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.7K
Studying Cell Rolling Trajectories on Asymmetric Receptor Patterns
04:24

Studying Cell Rolling Trajectories on Asymmetric Receptor Patterns

Published on: February 13, 2011

9.9K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Population Health

Background:

  • Latent class trajectory modelling (LCTM) is an emerging epidemiological tool for analyzing life-course exposures.
  • Heterogeneous populations can be simplified into homogeneous patterns using LCTM.
  • Selecting the optimal LCTM from multiple possibilities (based on structure, class number, and trajectory properties) presents a challenge.

Purpose of the Study:

  • To develop and present a systematic framework for constructing and selecting a 'core' Latent Class Trajectory Model (LCTM).
  • To enhance the generalizability and reliability of LCTM findings in epidemiological research.
  • To provide a standardized approach for model selection in complex population studies.

Main Methods:

  • An eight-step framework was developed, encompassing scoping, class number refinement, model structure specification (fixed to random effects), adequacy assessment, graphical presentation, and discrimination tools.
  • The framework was illustrated using body mass index (BMI) data from the NIH-AARP cohort, including baseline and recalled BMI at younger ages.
  • Methods included statistical modeling, graphical analysis, discrimination metrics (degree of separation, residual plots), clinical characterization, and sensitivity analyses.

Main Results:

  • A five-class model was derived for both men and women from over 288,000 participants.
  • The favored model structure was proportional random quadratic (model F), with favorable properties also noted for the unrestricted random quadratic structure (model G).
  • Moderate concordance was observed between models F and G (Cohen's κ: men, 0.57; women, 0.65), but poor concordance with other model structures, supporting the selected model through various assessments.

Conclusions:

  • A robust framework for constructing and selecting a core LCTM has been proposed.
  • This systematic approach facilitates the identification of a preferred model, improving the generalizability of epidemiological findings.
  • The framework aids researchers in navigating the complexities of LCTM selection, leading to more reliable life-course exposure analyses.