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

Clinical Trials01:16

Clinical Trials

10.8K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
10.8K
Clinical Trials: Overview01:11

Clinical Trials: Overview

5.0K
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...
5.0K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.6K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.6K
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

250
Body:Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to...
250
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

417
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
417
Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

497
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
497

You might also read

Related Articles

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

Sort by
Same author

Large Language Models for Clinical Narrative Processing: Methods, Applications, and Challenges.

Methods and protocols·2026
Same author

MetaMind: A multi-agent transformer-driven framework for automated network meta-analyses.

PloS one·2026
Same author

Quantile Effect on Duration of Response: A Zero-Inflated Censored Regression Approach.

Pharmaceutical statistics·2025
Same author

Bayesian dynamic power prior borrowing for augmenting a control arm for survival analysis.

Journal of biopharmaceutical statistics·2025
Same author

Assessing predictive probability of success for future clinical trials.

Journal of biopharmaceutical statistics·2025
Same author

Clinical Information Extraction with Large Language Models: A Case Study on Organ Procurement.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2025
Same journal

A Bayesian Optimal Interval Design Considering Efficacy and Toxicity in Early Phase Basket Trials.

Pharmaceutical statistics·2026
Same journal

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

Pharmaceutical statistics·2026
Same journal

Harmonic Fowlkes-Mallows Index for Medical Diagnostics Tests and Optimal Cut-Off Point Selection of Binary Diseases.

Pharmaceutical statistics·2026
Same journal

Early Phase Dose-Finding Designs for CAR-T Cell Therapies.

Pharmaceutical statistics·2026
Same journal

Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints.

Pharmaceutical statistics·2026
Same journal

CUI-MET: A Clinical Utility Index Based Analysis and Decision Framework for Dose Optimization in Multiple-Dose, Multiple-Outcome Randomized Trials.

Pharmaceutical statistics·2026
See all related articles

Related Experiment Video

Updated: Feb 7, 2026

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

15.4K

Propensity score matched augmented controls in randomized clinical trials: A case study.

Junjing Lin1, Margaret Gamalo-Siebers2, Ram Tiwari3

  • 1AbbVie, North Chicago, IL, 60064, USA.

Pharmaceutical Statistics
|August 2, 2018
PubMed
Summary
This summary is machine-generated.

Randomized controlled trials (RCTs) are often infeasible. Propensity score matching can augment clinical trial data using external controls, improving evidence for therapeutic product approval.

Keywords:
causal inferencedata augmentationhistorical controlmatchingpropensity score

More Related Videos

Author Spotlight: Advancements and Challenges in Surgical Treatments for Postamputation Pain
03:26

Author Spotlight: Advancements and Challenges in Surgical Treatments for Postamputation Pain

Published on: March 8, 2024

3.6K
Author Spotlight: Regenerative Peripheral Nerve Interface (RPNI) Surgery in Postamputation Pain Management
03:53

Author Spotlight: Regenerative Peripheral Nerve Interface (RPNI) Surgery in Postamputation Pain Management

Published on: March 15, 2024

2.9K

Related Experiment Videos

Last Updated: Feb 7, 2026

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

15.4K
Author Spotlight: Advancements and Challenges in Surgical Treatments for Postamputation Pain
03:26

Author Spotlight: Advancements and Challenges in Surgical Treatments for Postamputation Pain

Published on: March 8, 2024

3.6K
Author Spotlight: Regenerative Peripheral Nerve Interface (RPNI) Surgery in Postamputation Pain Management
03:53

Author Spotlight: Regenerative Peripheral Nerve Interface (RPNI) Surgery in Postamputation Pain Management

Published on: March 15, 2024

2.9K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Health Economics

Background:

  • Regulatory approval requires robust evidence of therapeutic effectiveness, typically from randomized controlled trials (RCTs).
  • RCTs face feasibility challenges including size, cost, duration, patient preference, and ethical considerations.
  • Rare diseases and multidrug-resistant infections pose specific challenges for adequately powered RCTs due to limited patient enrollment.

Purpose of the Study:

  • To explore propensity score matching methods for augmenting clinical trial data.
  • To utilize external or historical control data when RCTs are impractical or underpowered.
  • To address disproportionate or asymmetric randomization in clinical trials.

Main Methods:

  • Application of two propensity score matching schemes.
  • Selection and "borrowing" of patients from external control data (historical controls, observational studies, registries).
  • Matching treatment and control units based on observable pretreatment characteristics (covariates).

Main Results:

  • Demonstrated feasibility of using propensity score matching with external controls.
  • Successfully augmented trial data in a real-world clinical example.
  • Facilitated model-based pairing of similar treatment and control units.

Conclusions:

  • Propensity score matching offers a viable strategy to incorporate external data in clinical trials.
  • This approach can enhance the evidence base for therapeutic products, especially in challenging scenarios.
  • External data augmentation can support regulatory approval when traditional RCTs are limited.