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

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 researchers try to extrapolate results...
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:
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Odds Ratio01:09

Odds Ratio

The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...

You might also read

Related Articles

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

Sort by
Same author

Combining Observational Studies to Reduce Multiple Biases.

Epidemiology (Cambridge, Mass.)·2026
Same author

Ionising radiation and cancer: a UN review of the recent epidemiological evidence.

The Lancet. Oncology·2026
Same author

Spatial analysis of residential location at birth, PFAS in public water, and childhood cancers in Southern California (2000-2019).

Journal of exposure science & environmental epidemiology·2026
Same author

Corrigendum to 'Advances in the Basic Sciences in Thoracic Oncology in the Last 20 Years and Their Translational Impact' [Journal of Thoracic Oncology Volume 21 Issue 1 (2026) 41-76].

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer·2026
Same author

Advances in the Basic Sciences in Thoracic Oncology in the Last 20 Years and Their Translational Impact.

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer·2026
Same author

Uranium mining and lung cancer: a legacy of the nuclear age.

Carcinogenesis·2025

Related Experiment Video

Updated: May 16, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Random effects regression models for trends in standardised mortality ratios.

David B Richardson1, Stephen R Cole, Haitao Chu

  • 1Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. david_richardson@unc.edu

Occupational and Environmental Medicine
|November 17, 2012
PubMed
Summary

Standardised mortality ratios (SMRs) can be misleading when comparing different time periods due to changing demographics. A new random effects model helps reduce this bias in occupational hazard evaluations.

Related Experiment Videos

Last Updated: May 16, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Epidemiology
  • Occupational Health
  • Biostatistics

Background:

  • Standardised mortality ratios (SMRs) are crucial for evaluating occupational hazards.
  • Comparisons of SMRs across calendar periods can be misleading due to age distribution changes over time.

Purpose of the Study:

  • To propose a random effects model to mitigate bias in SMR comparisons.
  • To address the limitations of traditional SMR analyses in epidemiological studies.

Main Methods:

  • Development and application of a random effects model.
  • Illustration using data from workers at Oak Ridge National Laboratory.

Main Results:

  • The model yields results equivalent to classical SMR analysis when homogeneity exists.
  • The random effects version of SMRs conforms better to internal analysis of rate ratios when homogeneity decreases.

Conclusions:

  • The proposed random effects model effectively reduces potential bias in SMR comparisons.
  • This method offers a more reliable approach for epidemiological evaluations of occupational hazards.