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

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
Bootstrapping01:24

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...

You might also read

Related Articles

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

Sort by
Same author

Perinatal outcomes in lesbian couples employing shared motherhood IVF compared with those performing artificial insemination with donor sperm.

Human reproduction (Oxford, England)·2023
Same author

Remdesivir for Severe Coronavirus Disease 2019 (COVID-19) Versus a Cohort Receiving Standard of Care.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2020
Same author

Controlling alpha for mixed effects models for repeated measures.

Journal of biopharmaceutical statistics·2018
Same author

Exogenous Restoration of TUSC2 Expression Induces Responsiveness to Erlotinib in Wildtype Epidermal Growth Factor Receptor (EGFR) Lung Cancer Cells through Context Specific Pathways Resulting in Enhanced Therapeutic Efficacy.

PloS one·2015
Same author

Clinical practice guidelines for subarachnoid haemorrhage. Diagnosis and treatment.

Neurologia (Barcelona, Spain)·2015
Same author

Analysis of Partially Incomplete Tables of Breast Cancer Characteristics with an Ordinal Variable.

Journal of statistical theory and practice·2015

Related Experiment Video

Updated: May 28, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Borrowing strength with nonexchangeable priors over subpopulations.

L G Leon-Novelo1, B Nebiyou Bekele, P Müller

  • 1Department of Statistics, University of Florida, 102 Griffin-Floyd Hall, PO Box 118545, Gainesville, Florida 32611, USA. luis@stat.ufl.edu

Biometrics
|November 2, 2011
PubMed
Summary

This study introduces a new Bayesian model for phase II clinical trials with non-exchangeable disease subtypes. The model effectively borrows information across subtypes, improving success probability estimates for rare conditions like sarcoma.

More Related Videos

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

Last Updated: May 28, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Bayesian Nonparametrics

Background:

  • Phase II clinical trials often involve diseases with multiple subtypes.
  • Small sample sizes within subtypes necessitate information sharing.
  • Traditional hierarchical models struggle with non-exchangeable subtypes.

Purpose of the Study:

  • To develop a nonparametric Bayesian model for phase II trials with non-exchangeable disease subtypes.
  • To estimate experimental therapy success probabilities for each subtype.
  • To enable effective information borrowing across subtypes with varying prognoses.

Main Methods:

  • Introduced a random partition model, a variation of the product partition model.
  • Modeled a nonexchangeable prior structure for disease subtypes.
  • Utilized a clustering approach considering all patients across subtypes.

Main Results:

  • The proposed model effectively borrows information, prioritizing subtypes with similar prognoses.
  • Simulation studies showed comparable performance to fixed partition models when assumptions hold.
  • The new model outperformed the competing model when subtype assumptions were violated.

Conclusions:

  • The nonparametric Bayesian model addresses challenges in phase II trials with non-exchangeable subtypes.
  • This approach is particularly valuable for rare diseases like sarcoma with small patient groups.
  • The model enhances the estimation of treatment efficacy across diverse disease subtypes.