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

Relative Risk01:12

Relative Risk

99
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
99
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

106
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,...
106
Randomized Experiments01:13

Randomized Experiments

6.6K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.6K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

99
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...
99
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

75
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.
75
Censoring Survival Data01:09

Censoring Survival Data

50
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...
50

You might also read

Related Articles

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

Sort by
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

Efficient randomized adaptive designs for multi-arm clinical trials.

Statistical methods in medical research·2025
Same author

Quantization-based chained privacy-preserving federated learning.

Scientific reports·2025
Same author

FLT3LG modulates the infiltration of immune cells and enhances the efficacy of anti-PD-1 therapy in lung adenocarcinoma.

BMC cancer·2025
Same author

Thermoplastic Polyureas with Excellent Mechanical Properties Synthesized From CO<sub>2</sub>-Based Oligourea.

Macromolecular rapid communications·2025
Same author

IINS Vs CALLY Index: A Battle of Prognostic Value in NSCLC Patients Following Surgery.

Journal of inflammation research·2025

Related Experiment Video

Updated: May 17, 2025

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

14.3K

Statistical inference on the relative risk following covariate-adaptive randomization.

Fengyu Zhao1, Yang Liu2, Feifang Hu1

  • 1Department of Statistics, The George Washington University, Washington, DC 20052, United States.

Biometrics
|April 7, 2025
PubMed
Summary

Covariate-adaptive randomization (CAR) in clinical trials can lead to conservative relative risk tests. This study introduces new model-based and model-robust methods to improve standard error estimation and enhance hypothesis testing accuracy for relative risk inference.

Keywords:
covariate-adaptive randomizationmodel-based adjustmentmodel-robust adjustmentreduced type I errorrelative risk

More Related Videos

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

2.0K
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

10.0K

Related Experiment Videos

Last Updated: May 17, 2025

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

14.3K
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

2.0K
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

10.0K

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Statistical Inference

Background:

  • Covariate-adaptive randomization (CAR) is crucial for balancing treatment groups in clinical trials based on baseline covariates.
  • While average treatment effects are well-studied, inference for relative risk under CAR remains less explored.
  • Existing methods for relative risk analysis in CAR may exhibit conservative properties.

Purpose of the Study:

  • To examine a covariate-adjusted estimate of relative risk under CAR.
  • To investigate the properties of hypothesis tests for relative risk in CAR experiments.
  • To develop and validate improved methods for relative risk inference in CAR.

Main Methods:

  • Derivation of theoretical properties for covariate-adjusted relative risk across various CAR procedures.
  • Introduction of model-based and model-robust methods for enhanced standard error estimation.
  • Conducting extensive numerical studies to validate theoretical findings and proposed methods.

Main Results:

  • Conventional hypothesis tests for relative risk under CAR were found to be conservative, resulting in reduced Type I error rates.
  • The proposed model-based and model-robust methods effectively enhance standard error estimation.
  • Demonstrated validity and favorable properties of the adjusted tests through numerical simulations.

Conclusions:

  • The study provides a theoretical framework for covariate-adjusted relative risk inference in CAR.
  • New statistical methods are proposed to address the conservativeness of conventional tests.
  • The developed methods offer improved accuracy and reliability for relative risk analysis in CAR clinical trials.