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

Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

516
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%...
516
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

5.7K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
5.7K
Controls in Experiments01:13

Controls in Experiments

14.6K
When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
14.6K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

334
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,...
334
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

114
Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
114
Study Design in Statistics01:15

Study Design in Statistics

9.9K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
9.9K

You might also read

Related Articles

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

Sort by
Same author

Synergy Area With FDR-Controlled Evaluation (SAFE) to Robustly Assess Safety Profile in Clinical Trials.

Statistics in medicine·2026
Same author

Prospectively Specified Adaptive Bayesian Borrowing: Considerations, Methodologies, and Implementations.

Pharmaceutical statistics·2025
Same author

Prior Effective Sample Size When Borrowing on the Treatment Effect Scale.

Statistics in medicine·2025
Same author

An Integrated and Coherent Framework for Point Estimation and Hypothesis Testing With Concurrent Controls in Platform Trials.

Statistics in medicine·2025
Same author

A general, flexible, and harmonious framework to construct interpretable functions in regression analysis.

Biometrics·2025
Same author

A class of computational methods to reduce selection bias when designing Phase 3 clinical trials.

Statistics in medicine·2024

Related Experiment Video

Updated: Dec 24, 2025

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
04:53

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

11.1K

Modified Goldilocks Design with strict type I error control in confirmatory clinical trials.

Tianyu Zhan1, Hongtao Zhang2, Alan Hartford3

  • 1Data and Statistical Sciences, AbbVie Inc ., North Chicago, IL, USA.

Journal of Biopharmaceutical Statistics
|April 17, 2020
PubMed
Summary

Modified Goldilocks Design (MGD) analytically controls type I error in adaptive trials, unlike original Goldilocks Design. This Bayesian adaptive design offers similar statistical power for clinical trials.

Keywords:
Bayesian adaptive designcombination test approachsample size selection

More Related Videos

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.1K
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

7.8K

Related Experiment Videos

Last Updated: Dec 24, 2025

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
04:53

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

11.1K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.1K
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

7.8K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Bayesian Statistics

Background:

  • Goldilocks Design (GD) is a Bayesian adaptive design that uses predictive probability for sample size adaptation.
  • GD requires extensive simulations to control Type I error for specific null space subsets, limiting its use in confirmatory trials.

Purpose of the Study:

  • To propose a Modified Goldilocks Design (MGD) for adaptive clinical trials.
  • To analytically control Type I error across the entire null space, enhancing applicability for confirmatory trials.

Main Methods:

  • The MGD applies the conditional invariance principle.
  • It utilizes a combination test approach on p-values derived from independent cohorts.
  • This method analytically controls Type I error without extensive pre-study simulations.

Main Results:

  • The MGD analytically controls Type I error across the entire null space.
  • Simulation studies demonstrate that MGD maintains statistical power comparable to the original GD.
  • The design was successfully applied to a time-to-event oncology trial.

Conclusions:

  • The Modified Goldilocks Design provides an analytical method for Type I error control in Bayesian adaptive trials.
  • MGD is a viable alternative to GD for confirmatory trials, offering improved Type I error control with similar power.
  • This approach simplifies the design process by reducing reliance on extensive simulations.