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

Random and Systematic Errors01:20

Random and Systematic Errors

11.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
11.2K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

237
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
237
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

151
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,...
151
Clinical Trials: Overview01:11

Clinical Trials: Overview

3.1K
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...
3.1K
Randomized Experiments01:13

Randomized Experiments

7.1K
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...
7.1K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.6K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control.

Pharmaceutical statistics·2026
Same author

A randomized controlled Phase I de-escalation trial of molnupiravir and nirmatrelvir/ritonavir combination for mild-moderate SARS-CoV-2 infection.

The Journal of antimicrobial chemotherapy·2026
Same author

Acceptability and Implementation of a Primary Care Health Check for Autistic People: Findings From Evaluation Questionnaires and Interviews.

Autism : the international journal of research and practice·2026
Same author

On the Inclusion of Non-Concurrent Controls in Platform Trials With an Interim Analysis.

Statistics in medicine·2026
Same author

A Pilot Study to Explore Length and Readability Characteristics of Subject Information Sheets/Informed Consent Forms of Clinical Trial Applications in the EU.

Clinical and translational science·2026
Same author

When randomization is not random: Allocation bias in small sample, group sequential randomized clinical trials.

Statistical methods in medical research·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: Aug 4, 2025

Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

8.7K

Online error rate control for platform trials.

David S Robertson1, James M S Wason2, Franz König3

  • 1MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.

Statistics in Medicine
|April 2, 2023
PubMed
Summary
This summary is machine-generated.

Online error rate control methods can manage type I error inflation in platform trials. This approach offers improved power compared to Bonferroni correction while maintaining robust error control for multiple hypotheses tested over time.

Keywords:
multiple testingonline hypothesis testingplatform trialtype I error rate

More Related Videos

Methods to Test Visual Attention Online
09:44

Methods to Test Visual Attention Online

Published on: February 19, 2015

11.9K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K

Related Experiment Videos

Last Updated: Aug 4, 2025

Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

8.7K
Methods to Test Visual Attention Online
09:44

Methods to Test Visual Attention Online

Published on: February 19, 2015

11.9K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methodology

Background:

  • Platform trials evaluate multiple treatments under one master protocol, adding arms over time.
  • Multiple treatment comparisons in platform trials can inflate the overall type I error rate.
  • Hypothesis testing occurs at different times and may not be pre-specified, complicating error control.

Purpose of the Study:

  • To apply online error rate control methodology to platform trials.
  • To evaluate the performance of online error rate control in managing multiplicity.
  • To provide recommendations for practical implementation in platform trials.

Main Methods:

  • Utilized the online multiple hypothesis testing framework for sequential hypothesis testing.
  • Developed and simulated algorithms for online control of the false discovery rate and familywise error rate (FWER).
  • Compared online error rate control methods against uncorrected testing and Bonferroni correction.

Main Results:

  • Online error rate control algorithms demonstrated substantially lower FWER than uncorrected testing.
  • These methods achieved noticeable gains in statistical power compared to Bonferroni correction.
  • Simulations showed the practical utility and performance of online error rate control in platform trial settings.

Conclusions:

  • Online error rate control is a viable solution for multiplicity issues in platform trials.
  • This methodology offers a balance between stringent error control and enhanced statistical power.
  • The findings support the adoption of online error rate control for efficient platform trial design and analysis.