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

Determination of Expected Frequency01:08

Determination of Expected Frequency

2.3K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.3K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

3.2K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
3.2K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

333
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...
333
Contingency Table01:29

Contingency Table

2.7K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
2.7K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

215
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.
215
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

4.7K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
4.7K

You might also read

Related Articles

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

Sort by
Same author

Simulating survival data with predefined censoring rates under a mixture of non-informative right censoring schemes.

Communications in statistics: Simulation and computation·2026
Same author

Mitomycin-C for HPV-Positive and HPV-Negative Platinum-Refractory, Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma: A Phase 2 Trial.

Cancers·2025
Same author

Cell-of-Origin Subtype Predicts Response to Polatuzumab Vedotin in Large B-cell Lymphoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025
Same author

Eliciting Opinions on Health Messaging During the COVID-19 Pandemic: Qualitative Survey Study.

JMIR human factors·2023
Same author

Hematopoietic cell transplantation donor-derived memory-like NK cells functionally persist after transfer into patients with leukemia.

Science translational medicine·2022
Same author

<i>ATRAID</i> regulates the action of nitrogen-containing bisphosphonates on bone.

Science translational medicine·2020
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Oct 5, 2025

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

10.4K

Conditional or unconditional logistic regression for frequency matched case-control design?

Fei Wan1

  • 1Division of Public Health Sciences, Washington University in St. Louis, St. Louis, Missouri, USA.

Statistics in Medicine
|January 24, 2022
PubMed
Summary
This summary is machine-generated.

Frequency matching in case-control studies balances groups, but analysis methods differ. Unconditional logistic regression (ULR) can be more efficient than conditional logistic regression (CLR) with continuous matching factors.

Keywords:
biascase-control designconditional logistic regressionfrequency matchingunconditional logistic regression

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

232

Related Experiment Videos

Last Updated: Oct 5, 2025

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

10.4K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

232

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Frequency matching is a common technique in case-control studies to balance covariates between cases and controls.
  • Applied researchers often believe unconditional logistic regression (ULR) is sufficient for analyzing frequency-matched data, deeming conditional logistic regression (CLR) unnecessary.

Purpose of the Study:

  • To compare the performance of ULR and CLR in frequency-matched case-control studies.
  • To clarify the justification for using ULR over CLR in such designs.
  • To provide an intuitive comparison based on weighted sampling principles.

Main Methods:

  • Frequency matching was viewed from a weighted sampling perspective.
  • Outcome models were derived for matched data under two scenarios: categorical matching and categorized continuous matching.
  • The performance of ULR and CLR was evaluated based on simplicity, unbiasedness, and efficiency.

Main Results:

  • In both categorical and categorized continuous matching scenarios, the derived outcome model is a logit model with stratum-specific intercepts.
  • Correctly specified ULR can be more efficient than CLR, especially when continuous matching factors are used.
  • CLR offers a more practical approach due to its reduced dependence on specific modeling choices.

Conclusions:

  • The choice between ULR and CLR for frequency-matched case-control studies depends on the nature of matching variables and desired practicality.
  • While ULR may offer efficiency gains with continuous matching factors, CLR provides a more robust and less assumption-dependent analysis.
  • Further research may be needed to fully elucidate the optimal analytical strategy in various frequency-matched study designs.