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

Prevalence and Incidence01:08

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health condition at a...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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, controlled...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Introduction to Epidemiology01:26

Introduction to Epidemiology

Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
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...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.

You might also read

Related Articles

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

Sort by
Same author

An approach to nonparametric inference on the causal dose-response function.

Journal of causal inference·2026
Same author

Sequential invitations to FOBT screening and colorectal cancer incidence.

Scientific reports·2026
Same author

Powering RCTs for Marginal Effects With GLMs Using Prognostic Score Adjustment.

Statistics in medicine·2026
Same author

Machine learning to optimize precision in the analysis of randomized trials: A journey in pre-specified, yet data-adaptive learning.

Clinical trials (London, England)·2026
Same author

Assessing Treatment Effects in Observational Data With Missing Confounders: A Comparative Study of Practical Doubly-Robust and Traditional Missing Data Methods.

Statistics in medicine·2026
Same author

Epistatic contributions to human traits via transcription factor mechanisms.

medRxiv : the preprint server for health sciences·2025
Same journal

Targeted maximum likelihood estimation (TMLE) in regulatory submissions and research: a landscape analysis.

The international journal of biostatistics·2026
Same journal

Predicting birth weight by multivariate functional principal component regressions.

The international journal of biostatistics·2026
Same journal

Robust median regression for count data with general lower truncation using a contaminated discrete Weibull model.

The international journal of biostatistics·2026
Same journal

Handling the uncertainty issue of missingness via a mixture-structure-based method.

The international journal of biostatistics·2026
Same journal

Statistical method for pooling categorical biomarker data from multi-center matched/nested case-control studies.

The international journal of biostatistics·2026
Same journal

Prognostic score methods for the estimation of the average causal effect.

The international journal of biostatistics·2026
See all related articles

Related Experiment Video

Updated: May 23, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Estimation based on case-control designs with known prevalence probability.

Mark J van der Laan1

  • 1University of California, Berkeley, CA, USA.

The International Journal of Biostatistics
|April 3, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel weighting method for case-control studies, improving the estimation of treatment effects and genetic marker associations. The approach ensures efficient and robust analysis even with low case prevalence.

More Related Videos

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

Related Experiment Videos

Last Updated: May 23, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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

Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical modeling

Background:

  • Case-control studies are widely used for binary outcomes, especially when cases are rare.
  • These studies often involve biased sampling, posing challenges for accurate effect estimation.
  • Existing methods struggle with efficiency, particularly in matched or unmatched case-control designs.

Purpose of the Study:

  • To present a general estimation method for case-control studies using a weighting scheme.
  • To enable the adaptation of prospective sampling estimators for case-control data.
  • To derive double robust, locally efficient targeted maximum likelihood estimators (TMLEs).

Main Methods:

  • Developed a general weighting methodology applicable to case-control sampling.
  • Utilized known prevalence probability, conditional on matching variables if applicable.
  • Mapped prospective estimation methods to case-control estimation frameworks.

Main Results:

  • The proposed weighting scheme ensures efficient estimators for both matched and unmatched case-control designs.
  • Demonstrated the derivation of double robust locally efficient TMLEs for causal relative risk and odds ratio.
  • The methodology is robust across various case-control sampling scenarios.

Conclusions:

  • The novel weighting method enhances the accuracy and efficiency of case-control studies.
  • This approach provides robust estimation of causal effects, particularly valuable in epidemiology and genetics.
  • The generalized methodology offers flexibility and broad applicability for complex study designs.