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

Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

6.1K
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.
6.1K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

615
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%...
615
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

514
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
514
Bias01:22

Bias

7.4K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.4K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

7.0K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
7.0K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.1K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Intravenous lanadelumab for the treatment of moderately ill COVID-19 patients.

British journal of clinical pharmacology·2026
Same author

On the Two-Step Hybrid Design for Augmenting Randomized Trials Using Real-World Data.

Statistics in biopharmaceutical research·2025
Same author

Optimising clinical trial methods for complex regional pain syndrome: a methodological framework (OptiMeth-CRPS).

Pain reports·2025
Same author

Optimizing Patient Registries for Regulatory Decision Making - Key Learnings From an HMA/EMA Multistakeholder Workshop.

Clinical pharmacology and therapeutics·2025
Same author

SIMPATHIC: Accelerating drug repurposing for rare diseases by exploiting SIMilarities in clinical and molecular PATHology.

Molecular genetics and metabolism·2025
Same author

Decision-Making Criteria and Methods for Initiating Late-Stage Clinical Trials in Drug Development From a Multi-Stakeholder Perspective: A Scoping Review.

Clinical pharmacology and therapeutics·2025
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

A Bayesian phase I/II platform design with data augmentation accounting for delayed outcomes.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Feb 17, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

Dynamic borrowing through empirical power priors that control type I error.

Stavros Nikolakopoulos1, Ingeborg van der Tweel1, Kit C B Roes1

  • 1Department of Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands.

Biometrics
|December 12, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for using historical clinical trial data, especially in small populations. It controls the type I error rate when borrowing evidence from a single historical study.

Keywords:
Clinical trialsDynamic borrowingPower priorsType I error

More Related Videos

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.5K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K

Related Experiment Videos

Last Updated: Feb 17, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.5K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K

Area of Science:

  • Clinical Trials
  • Biostatistics
  • Statistical Modeling

Background:

  • Incorporating historical data in clinical trials is crucial, particularly for small populations with limited data and unknown heterogeneity.
  • Conventional evidence synthesis methods may be insufficient in these scenarios.

Purpose of the Study:

  • To propose a novel, simple method for estimating the power parameter in power priors when using a single historical dataset.
  • To ensure the control of type I error when borrowing evidence from historical clinical trial data.

Main Methods:

  • The proposed method utilizes predictive distributions to estimate the power parameter.
  • The parameterization allows type I error control by calibrating to data similarity between new and historical studies.
  • Demonstrated for normal responses in one or two-group settings, with straightforward generalization to other models.

Main Results:

  • A new method for estimating the power parameter in power priors is presented.
  • The method effectively controls type I error by assessing data similarity.
  • Applicable to clinical trial design and analysis, especially for small or data-scarce populations.

Conclusions:

  • The developed method offers a robust approach for incorporating single historical datasets into clinical trials.
  • It addresses the challenge of type I error inflation associated with borrowing historical data.
  • Facilitates more reliable evidence synthesis in challenging population settings.