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

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...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Censoring Survival Data01:09

Censoring Survival Data

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 reasons...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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 until a...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...

You might also read

Related Articles

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

Sort by
Same author

Early Amino Acid Intake in ELBW Infants: School-Aged Outcomes From a Randomized Controlled Trial.

Pediatrics·2026
Same author

Early Nutrition, Blood Amino Acids and Outcomes in Preterm Babies: Secondary Cohort Analysis of the ProVIDe RCT.

Nutrients·2026
Same author

Postnatal conditional growth and size and body composition at 24 months in moderate-and-late preterm children.

Pediatric research·2026
Same author

Machine Learning-Based Prediction Model Construction for Type 2 Diabetes Mellitus: A Comparison of Algorithms and Multilevel Risk Factor Analysis.

Journal of diabetes research·2026
Same author

Validation of Guillain-Barré syndrome case identification in three heterogeneous VAC4EU real-world data sources in Spain using the Brighton Collaboration criteria.

Vaccine·2026
Same author

Laboratory evaluation of the Allplex NG & DR assay for detection of <i>Neisseria gonorrhoeae</i> and associated azithromycin and ciprofloxacin resistance markers.

Sexually transmitted infections·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
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 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

Case-control analysis with a continuous outcome variable.

Yannan Jiang1, Alastair Scott, Chris J Wild

  • 1Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, New Zealand.

Statistics in Medicine
|November 11, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new linear-model-based method for analyzing dichotomized outcomes in case-control studies, improving statistical efficiency. This approach offers significant gains compared to traditional logistic regression, especially for odds ratios.

Related Experiment Videos

Last Updated: Jun 28, 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

Area of Science:

  • Biostatistics
  • Epidemiological Methods
  • Statistical Modeling

Background:

  • Continuous outcome variables are frequently dichotomized for analysis using logistic regression.
  • Previous work by Moser and Coombs (2004) offered efficient inferences for prospective studies by converting linear regression output.
  • Their method, however, was limited to prospective study designs.

Purpose of the Study:

  • To extend the efficient linear-model-based approach to case-control study designs.
  • To provide a statistical solution for analyzing dichotomized outcomes in case-control data.
  • To compare the efficiency of linear-model-based analyses versus logistic regression in case-control studies.

Main Methods:

  • Developed a linear-model-based method for analyzing dichotomized continuous outcomes in case-control studies.
  • Applied the method to estimate odds-ratio parameters.
  • Compared statistical efficiency gains with logistic regression analyses.

Main Results:

  • Achieved substantial gains in statistical efficiency, up to 240%, even with small to moderate odds ratios.
  • Demonstrated that differences in design efficiency between case-control and prospective studies are reduced with linear-model-based analyses.
  • Linear-model-based analyses show greater efficiency than logistic regression for case-control data.

Conclusions:

  • The proposed linear-model-based method provides an efficient alternative for analyzing dichotomized outcomes in case-control studies.
  • This approach significantly enhances statistical efficiency compared to traditional logistic regression.
  • Linear-model-based analyses minimize design efficiency differences between sampling strategies.