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

Clinical Trials01:16

Clinical Trials

10.9K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
10.9K
Clinical Trials: Overview01:11

Clinical Trials: Overview

5.0K
Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
5.0K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.6K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.6K
Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

12.8K
The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
12.8K
Myocarditis II: Clinical Features and Diagnostic Tests01:27

Myocarditis II: Clinical Features and Diagnostic Tests

334
Myocarditis is an inflammation of the heart muscle. The symptoms vary widely, encompassing asymptomatic presentations to severe, acute manifestations.Clinical PresentationAsymptomatic cases: In some instances, myocarditis may be asymptomatic, with the infection resolving without intervention. These cases often go undetected unless discovered incidentally through diagnostic imaging or tests conducted for other reasons.General Early Symptoms: Early symptoms of myocarditis are non-specific and can...
334
Pericarditis II: Clinical Features and Diagnostic Tests01:19

Pericarditis II: Clinical Features and Diagnostic Tests

344
Pericarditis is distinguished by inflammation of the pericardium, the fibrous sac that encases the heart. It can be acute, lasting less than six weeks, or chronic, persisting for over three months. Understanding its clinical manifestations and diagnostic findings is crucial for timely and effective management.Clinical ManifestationsWhile pericarditis can be asymptomatic, it usually presents with characteristic symptoms such as:Chest Pain: The most characteristic symptom of pericarditis is chest...
344

You might also read

Related Articles

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

Sort by
Same author

A historical note: Rediscovering an unpublished response to Korn and Freidlin (2011).

Statistical methods in medical research·2026
Same author

Efficient randomized adaptive designs for multi-arm clinical trials.

Statistical methods in medical research·2025
Same author

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

Biometrics·2025
Same author

Response-Adaptive Randomization Procedure in Clinical Trials with Surrogate Endpoints.

Statistics in medicine·2024
Same author

Group Response-Adaptive Randomization With Delayed and Missing Responses.

Statistics in medicine·2024
Same author

Robustness of response-adaptive randomization.

Biometrics·2024

Related Experiment Video

Updated: Feb 10, 2026

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.3K

Testing hypotheses under adaptive randomization with continuous covariates in clinical trials.

Xiaoming Li1, Jianhui Zhou1, Feifang Hu2

  • 11 University of Virginia, Charlottesville, USA.

Statistical Methods in Medical Research
|May 18, 2018
PubMed
Summary

This study provides a theoretical framework for adaptive clinical trial designs using continuous covariates. It shows these designs offer better power and valid hypothesis testing compared to traditional methods.

Keywords:
Covariate-adaptive designsconservative testcontinuous covariateslinear modelspowertype I error

More Related Videos

Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

6.3K
Author Spotlight: Advancements and Challenges in Surgical Treatments for Postamputation Pain
03:26

Author Spotlight: Advancements and Challenges in Surgical Treatments for Postamputation Pain

Published on: March 8, 2024

3.6K

Related Experiment Videos

Last Updated: Feb 10, 2026

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

2.3K
Computerized Adaptive Testing System of Functional Assessment of Stroke
05:21

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

6.3K
Author Spotlight: Advancements and Challenges in Surgical Treatments for Postamputation Pain
03:26

Author Spotlight: Advancements and Challenges in Surgical Treatments for Postamputation Pain

Published on: March 8, 2024

3.6K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Covariate-adaptive designs are crucial for balancing patient characteristics and maintaining randomization in clinical trials.
  • Existing research on adaptive designs primarily focuses on discrete covariates, with limited theoretical understanding for continuous covariates.
  • Discretizing continuous covariates can lead to significant information loss, necessitating advanced methods.

Purpose of the Study:

  • To establish a theoretical framework for hypothesis testing in adaptive clinical trial designs incorporating continuous covariates.
  • To analyze the asymptotic properties of test statistics for treatment effects and covariate significance under linear models.
  • To provide a foundation for understanding hypothesis testing in adaptive designs with continuous covariates, moving beyond simulation-based evidence.

Main Methods:

  • Development of a theoretical framework for hypothesis testing based on linear models for adaptive designs with continuous covariates.
  • Derivation of asymptotic distributions for test statistics under null and alternative hypotheses.
  • Conducting simulation studies across various covariate-adaptive designs, including p-value-based, Su's percentile, empirical cumulative-distribution, Kullback-Leibler divergence, and kernel-density methods.

Main Results:

  • Hypothesis testing for treatment effects in adaptive designs with continuous covariates demonstrates conservative properties with reduced type I error rates.
  • Adaptive designs exhibit superior statistical power compared to complete randomization methods.
  • The validity of hypothesis testing for covariate significance is maintained within these adaptive designs.

Conclusions:

  • The established theoretical framework provides a robust foundation for hypothesis testing in adaptive clinical trials with continuous covariates.
  • Adaptive designs offer improved efficiency and reliability in clinical trial analysis, particularly when dealing with continuous patient characteristics.
  • The findings support the use of adaptive designs for more powerful and valid hypothesis testing in modern clinical research.