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

Causality in Epidemiology01:21

Causality in Epidemiology

1.4K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.4K
Censoring Survival Data01:09

Censoring Survival Data

444
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...
444
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

1.0K
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
1.0K
Inductive Reasoning00:59

Inductive Reasoning

64.3K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
64.3K
Naturalistic Observations02:30

Naturalistic Observations

16.9K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
16.9K
Hindsight Biases01:12

Hindsight Biases

4.2K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
4.2K

You might also read

Related Articles

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

Sort by
Same author

Cardiovascular risk under low-fat, Mediterranean, and AHA diets: a target trial emulation in US adults.

The American journal of clinical nutrition·2026
Same author

Development and Validation of a Prediction Model for Cardiovascular Risk in Reproductive-Aged Women.

JACC. Advances·2026
Same author

Risk of thrombosis with thrombocytopaenia syndrome (TTS) after vaccination with AZD1222: a European VAC4EU post-authorisation safety study.

Vaccine·2026
Same author

Reply of the authors: Trial emulation and clinical trials.

Fertility and sterility·2026
Same author

Severe maternal morbidity and re-hospitalisation in the first postpartum year within Canada, 2008-2020: a population-based retrospective cohort study.

BMJ public health·2026
Same author

Optimizing future Telehealth mental health programs: a secondary analysis of a prospective cohort study to identify key predictors of intervention response in the Telehealth intervention program for older adults (TIP-OA).

BMC psychiatry·2026
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Dec 21, 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

10.9K

Causal inference for recurrent event data using pseudo-observations.

Chien-Lin Su1, Robert W Platt1, Jean-François Plante2

  • 1Department of Epidemiology, Biostatistics and Occupational Health, McGill University and Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montréal, Québec, Canada.

Biostatistics (Oxford, England)
|May 21, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces new methods to compare recurrent event data, adjusting for confounders in treatment assignments. The proposed estimators are consistent and reliable for analyzing complex observational studies.

Keywords:
Cumulative rate functionDoubly robust estimatorInverse probability of treatment weightingPseudo-observationsRecurrent event dataTwo-sample pseudo-score tests

More Related Videos

Simultaneous Long-term Recordings at Two Neuronal Processing Stages in Behaving Honeybees
13:55

Simultaneous Long-term Recordings at Two Neuronal Processing Stages in Behaving Honeybees

Published on: July 21, 2014

13.4K
Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

34.9K

Related Experiment Videos

Last Updated: Dec 21, 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

10.9K
Simultaneous Long-term Recordings at Two Neuronal Processing Stages in Behaving Honeybees
13:55

Simultaneous Long-term Recordings at Two Neuronal Processing Stages in Behaving Honeybees

Published on: July 21, 2014

13.4K
Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm
06:35

Examining Recall Memory in Infancy and Early Childhood Using the Elicited Imitation Paradigm

Published on: April 28, 2016

34.9K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Recurrent event data are common in observational studies, posing challenges due to potential confounding.
  • Comparing cumulative rate functions (CRFs) between groups requires methods that account for treatment assignment influenced by confounders.

Purpose of the Study:

  • To develop and evaluate statistical methods for comparing CRFs in the presence of treatment-assignment confounding.
  • To propose robust estimators and hypothesis tests for recurrent event data analysis.

Main Methods:

  • Development of inverse probability of treatment weighting, regression-based, and doubly robust estimators using pseudo-observations.
  • Introduction of a bootstrap approach for variance estimation and residual plots for model diagnostics.
  • Proposal of adjusted two-sample pseudo-score tests for comparing CRFs.

Main Results:

  • The proposed marginal regression and doubly robust estimators demonstrate consistency and asymptotic normality.
  • Simulation studies confirm the finite sample performance of the developed methods.
  • The technique is validated using hospital readmission data.

Conclusions:

  • The novel pseudo-observation-based methods effectively adjust for confounding in recurrent event data analysis.
  • The proposed estimators and tests provide reliable tools for comparing CRFs in observational studies.
  • This work offers practical solutions for analyzing complex health-related recurrent event data.