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

Genetic Drift03:33

Genetic Drift

40.6K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
40.6K
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
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

562
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...
562
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

121
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
121
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

686
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
686

You might also read

Related Articles

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

Sort by
Same author

Remote Monitoring Approaches to Reduce Readmissions After Infection and Sepsis: A Randomized Clinical Trial.

JAMA network open·2026
Same author

Ivermectin for Critically and Noncritically Ill Hospitalized Patients With COVID-19: Randomized, Embedded, Multifactorial Adaptive Platform Trial for Community-Acquired Pneumonia (REMAP-CAP).

Critical care medicine·2026
Same author

Erratum: Evaluation of Regorafenib in Newly Diagnosed and Recurrent Glioblastoma: GBM AGILE Phase II/III Bayesian Randomized Platform Trial.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

Oral Nirmatrelvir-Ritonavir for Covid-19 in Higher-Risk Outpatients.

The New England journal of medicine·2026
Same author

Evaluation of Regorafenib in Newly Diagnosed and Recurrent Glioblastoma: GBM AGILE Phase II/III Bayesian Randomized Platform Trial.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

Wastewater Surveillance for SARS-CoV-2 in Rural Kentucky, 2021-2023.

Viruses·2026
Same journal

A statistical evaluation of decision-making methods and the efficiency of Bayesian multi-arm multi-stage trials.

Clinical trials (London, England)·2026
Same journal

Accounting for non-adherence: A re-analysis of the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results trial.

Clinical trials (London, England)·2026
Same journal

Phase I design for partially ordered groups with late-onset toxicity.

Clinical trials (London, England)·2026
Same journal

Trial informed consent forms, the Declaration of Helsinki and the SPIRIT 2025 statement.

Clinical trials (London, England)·2026
Same journal

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Morning panel discussion).

Clinical trials (London, England)·2026
Same journal

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Afternoon panel discussion).

Clinical trials (London, England)·2026
See all related articles

Related Experiment Video

Updated: Aug 31, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K

The Bayesian Time Machine: Accounting for temporal drift in multi-arm platform trials.

Benjamin R Saville1,2, Donald A Berry1,3, Nicholas S Berry1

  • 1Berry Consultants, LLC, Austin, TX, USA.

Clinical Trials (London, England)
|August 22, 2022
PubMed
Summary
This summary is machine-generated.

Multi-arm platform trials can use time-adjusted analyses, like the Bayesian Time Machine, to account for temporal drift. This approach improves treatment effect estimation by including all control subjects, enhancing precision and statistical power.

Keywords:
Bayesian Time MachineTemporal driftadaptive platform trialconcurrent controlsmulti-arm clinical trialsnon-concurrent controlsstaggered entry

More Related Videos

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.1K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.0K

Related Experiment Videos

Last Updated: Aug 31, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.1K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.0K

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Pharmaceutical Research

Background:

  • Multi-arm platform trials involve simultaneous investigation of multiple agents with staggered entry/exit.
  • A key debate concerns using only concurrent or pooled (concurrent and non-concurrent) control subjects for analysis.
  • Potential bias from temporal drift over time is central to this debate.

Purpose of the Study:

  • To propose and evaluate time-adjusted analyses for multi-arm platform trials.
  • To introduce the "Bayesian Time Machine" for modeling temporal drift.
  • To compare the performance of time-adjusted analyses against concurrent and pooled control approaches.

Main Methods:

  • Development of time-adjusted analytical methods, including the "Bayesian Time Machine."
  • Modeling potential temporal drift across the entire study population.
  • Conducting a simulation study to assess performance metrics.

Main Results:

  • Time-adjusted analyses demonstrate superior estimation of treatment effects and favorable testing properties.
  • The Bayesian Time Machine offers enhanced precision and reduced mean square error compared to alternatives.
  • Potential risks include minor bias and slight Type I error inflation.

Conclusions:

  • The Bayesian Time Machine balances bias and precision by smoothing estimates and utilizing all data.
  • Pre-specified and carefully calibrated prior distributions are crucial for dynamic smoothing.
  • Analyses must account for trial-specific population, disease, and endpoint characteristics.