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

Actuarial Approach01:20

Actuarial Approach

345
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
345
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

511
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
511
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

677
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
677
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

505
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
505
Hazard Rate01:11

Hazard Rate

467
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
467
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

903
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...
903

You might also read

Related Articles

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

Sort by
Same author

Evaluating statistical models for overdispersed multi-omics data: a multiplex immunofluorescence case study.

American journal of epidemiology·2026
Same author

Site- and age-dependent associations between Fusobacterium nucleatum and colorectal cancer mortality.

Cancer·2026
Same author

Alcohol consumption and molecular subtypes of colorectal cancer: pooled observational and Mendelian randomization analyses.

The American journal of clinical nutrition·2026
Same author

MyGeneRisk Colon: A Web-Based Tool for Personalized Colorectal Cancer Risk Prediction Based on Genetics and Lifestyle.

medRxiv : the preprint server for health sciences·2026
Same author

Design of MOSAAIC (Multi-Ethnic Observational Study in American Asian and Pacific Islander Communities).

JACC. Asia·2026
Same author

TGF-β Pathway-Based Polygenic Risk Score Modifies the Association between Red Meat Intake and Colorectal Cancer Risk: Application of a Novel Pathway-Based PRS Method.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026

Related Experiment Video

Updated: Mar 8, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.4K

On estimation of time-dependent attributable fraction from population-based case-control studies.

Wei Zhao1, Ying Qing Chen2, Li Hsu2

  • 1Department of Biostatistics, University of Washington, Seattle, Washington, U.S.A.

Biometrics
|January 19, 2017
PubMed
Summary

Researchers developed a new method to estimate time-varying population attributable fraction (PAF) using case-control studies. This allows for better assessment of disease burden from modifiable exposures in population health research.

Keywords:
Case-control studyKernel smootherPopulation attributable fractionTime-varying

More Related Videos

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.7K
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

704

Related Experiment Videos

Last Updated: Mar 8, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.4K
Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.7K
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

704

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Population attributable fraction (PAF) quantifies disease burden from modifiable exposures.
  • Time-varying PAF offers insights into exposure impact over time in cohort studies.
  • Existing methods lack procedures for PAF estimation in case-control studies.

Purpose of the Study:

  • To develop a novel estimator for time-varying population attributable fraction (PAF) using population-based case-control study data.
  • To demonstrate the identifiability of time-varying PAF from case-control studies.
  • To provide a method for assessing disease burden attributable to exposures in commonly used study designs.

Main Methods:

  • Developed a novel PAF estimator combining odds ratio estimates from logistic regression.
  • Utilized kernel smoothing for density estimation of risk factor distribution conditional on failure times.
  • The estimator's consistency and asymptotic normality were theoretically established.

Main Results:

  • The proposed PAF estimator is consistent and asymptotically normal.
  • Empirical estimation of asymptotic variance is feasible.
  • Simulation studies confirmed the estimator's good performance in finite samples.

Conclusions:

  • Time-varying population attributable fraction (PAF) is identifiable and estimable from case-control studies.
  • The novel method provides a valuable tool for public health research and disease burden assessment.
  • The method was successfully illustrated using a colorectal cancer case-control study.