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

Sampling Methods: Overview01:06

Sampling Methods: Overview

3.9K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
3.9K
Censoring Survival Data01:09

Censoring Survival Data

649
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...
649
The Availability Heuristic01:08

The Availability Heuristic

7.3K
A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
7.3K
Sampling Plans01:23

Sampling Plans

1.3K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.3K

You might also read

Related Articles

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

Sort by
Same author

A Bayesian Causal Model for Matrix-Valued Exposures With Applications to Radiotherapy Planning.

Statistics in medicine·2026
Same author

Mapping neighbourhood-level drivers of type 2 diabetes for precision public health using predictive and causal machine learning.

Scientific reports·2026
Same author

Continuous-Time Causal Inference With Marked Point Process Weights: An Example on Sodium-Glucose Co-Transporters 2 Inhibitor Medications and Urinary Tract Infection.

Statistics in medicine·2025
Same author

Optimal characteristics of peer navigators: adapting peer-based intervention with street-involved youth in Canada and Kenya with the aim of increasing HIV prevention, testing and treatment.

Health research policy and systems·2025
Same author

Mediation CNN (Med-CNN) Model for High-Dimensional Mediation Data.

International journal of molecular sciences·2025
Same author

A Bayesian latent class approach to causal inference with longitudinal data.

Statistical methods in medical research·2024
Same journal

Shared frailty sieve estimation for dependent left truncated and interval censored data.

Lifetime data analysis·2026
Same journal

Functional win-fractions regression models for composite outcomes.

Lifetime data analysis·2026
Same journal

Variable selection in causal semiparametric transformation models with all-or-nothing treatment compliance.

Lifetime data analysis·2026
Same journal

Correction to: A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model.

Lifetime data analysis·2026
Same journal

Unobserved heterogeneity in threshold regression based on the hitting times of a reflected Brownian motion for recurrent hypoglycemia.

Lifetime data analysis·2026
Same journal

Variable selection with broken adaptive ridge regression for interval-censored competing risks data.

Lifetime data analysis·2026
See all related articles

Related Experiment Video

Updated: Mar 31, 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

11.2K

A case-base sampling method for estimating recurrent event intensities.

Olli Saarela1

  • 1Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON, M5T 3M7, Canada. olli.saarela@utoronto.ca.

Lifetime Data Analysis
|October 16, 2015
PubMed
Summary
This summary is machine-generated.

Case-base sampling offers an efficient alternative to traditional risk set sampling for estimating hazard regression models. This method accurately estimates hazard ratios and absolute hazards, even with time-dependent exposures and recurrent events.

Keywords:
Case-base samplingConditional logistic regressionHazard regressionRecurrent eventsSelf-matching

More Related Videos

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.5K
Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.4K

Related Experiment Videos

Last Updated: Mar 31, 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

11.2K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.5K
Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.4K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical modeling

Background:

  • Risk set sampling is a common method for estimating hazard regression models.
  • Estimating absolute hazards, in addition to hazard ratios, is often desirable.
  • Time-dependent exposures and recurrent events present unique challenges in statistical analysis.

Purpose of the Study:

  • To present case-base sampling as an alternative to risk set sampling for hazard regression.
  • To demonstrate the utility of case-base sampling for time-dependent exposures and recurrent events.
  • To compare the efficiency of case-base sampling with standard methods.

Main Methods:

  • Case-base sampling selects discrete person-time coordinates from follow-up data.
  • This approach yields a likelihood expression similar to logistic regression.
  • The partial likelihood for outcome event intensity is shown to have asymptotic likelihood properties.

Main Results:

  • Case-base sampling provides an alternative to risk set sampling for hazard regression.
  • The method is applicable to time-dependent exposures and recurrent adverse events.
  • Simulations indicate minimal information loss compared to standard methods, suggesting high efficiency.

Conclusions:

  • Case-base sampling is a viable and efficient alternative for hazard regression modeling.
  • The method is particularly useful when absolute hazards are of interest.
  • This approach offers a robust framework for analyzing complex epidemiological data.