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

Survival Tree01:19

Survival Tree

85
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
85
Statgraphics01:10

Statgraphics

128
Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
128
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
Appropriate sampling methods ensure 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 cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.6K
Biostatistics: Overview01:20

Biostatistics: Overview

241
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
241
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

550
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...
550

You might also read

Related Articles

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

Sort by
Same author

Systematic Evaluation of Data and Trial Fitness for Oncology Trial Emulation: Empirical Findings from the CARE Initiative.

Clinical pharmacology and therapeutics·2026
Same author

A Plasmode Simulation-Based Bias Analysis for Residual Confounding by Unmeasured Variables Leveraging Information-Rich Subsets.

Pharmacoepidemiology and drug safety·2026
Same author

Adaptive Multi-Wave Sampling for Efficient Chart Validation.

Clinical epidemiology·2026
Same author

Characterization and comparison of structured and unstructured electronic health record data mapped to MedDRA for post-marketing surveillance.

JAMIA open·2026
Same author

Post hoc Population Standardization of Trial Emulation Studies in Claims Data: An RCT-DUPLICATE Analysis.

Clinical pharmacology and therapeutics·2026
Same author

Enhancing cause of death prediction: development and validation of machine learning models using multimodal data across multiple health-care sites.

JAMIA open·2026
Same journal

Comparative Effectiveness of Oral Fluoropyrimidines Versus FOLFOX as Adjuvant Therapy for Stage III Colon Cancer: A Retrospective Cohort Study Using Overlap-Weighted Restricted Mean Survival Time Analysis.

Pharmacoepidemiology and drug safety·2026
Same journal

Association Between EGFR-TKI-Associated Skin Rash and Recorded Mortality in Non-Small Cell Lung Cancer: A Real-World Analysis Accounting for Immortal Time Bias.

Pharmacoepidemiology and drug safety·2026
Same journal

Nationwide Trends in Opioid Consumption in Costa Rica, 2017-2024: Implications for Regulatory Policy and Public Health.

Pharmacoepidemiology and drug safety·2026
Same journal

Mortality in Castration Resistant Prostate Cancer Patients With and Without Pre-Existing Cardiovascular Disease Receiving Oral Androgen Receptor Pathway Inhibitors.

Pharmacoepidemiology and drug safety·2026
Same journal

Trajectories Associated With the Use and Deprescription of Benzodiazepines and Z-Drugs in a Network of Geriatric Outpatient Clinics.

Pharmacoepidemiology and drug safety·2026
Same journal

Provider Perspectives to Inform a Patient-Facing Decision Aid on Oral Corticosteroid Use During Pregnancy and Evaluation of the Aid.

Pharmacoepidemiology and drug safety·2026
See all related articles

Related Experiment Video

Updated: Jul 1, 2025

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

1.0K

An open-source implementation of tree-based scan statistics.

Massimiliano Russo1,2, Shirley V Wang2

  • 1Department of Statistics, The Ohio State University, Columbus, Ohio, USA.

Pharmacoepidemiology and Drug Safety
|March 7, 2024
PubMed
Summary
This summary is machine-generated.

We created an open-source R package for tree-based scan statistics (TBSS) analyses. This tool helps identify adverse drug or vaccine effects by screening health outcomes, enhancing public health surveillance.

Keywords:
data mininghypothesis generationscan statisticssurveillancetree variable

More Related Videos

Clock Scan Protocol for Image Analysis: ImageJ Plugins
07:19

Clock Scan Protocol for Image Analysis: ImageJ Plugins

Published on: June 19, 2017

17.4K
ScanLag: High-throughput Quantification of Colony Growth and Lag Time
07:47

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Published on: July 15, 2014

16.2K

Related Experiment Videos

Last Updated: Jul 1, 2025

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

1.0K
Clock Scan Protocol for Image Analysis: ImageJ Plugins
07:19

Clock Scan Protocol for Image Analysis: ImageJ Plugins

Published on: June 19, 2017

17.4K
ScanLag: High-throughput Quantification of Colony Growth and Lag Time
07:47

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Published on: July 15, 2014

16.2K

Area of Science:

  • Pharmacovigilance and drug safety analysis.
  • Statistical data mining and computational epidemiology.

Background:

  • Tree-based scan statistics (TBSS) are advanced data mining methods utilized by regulatory agencies like the FDA and CDC.
  • TBSS are crucial for identifying unexpected adverse effects from drugs or vaccines by analyzing aggregated health outcomes while managing multiple comparisons.

Purpose of the Study:

  • To develop and release an open-source R package that implements the general framework of tree-based scan statistics (TBSS).
  • To provide user-friendly functions for common TBSS methods, facilitating broader adoption and application in drug safety research.

Main Methods:

  • The R package encodes the adaptable TBSS framework, including hierarchical outcome structures, test statistics, null distribution simulation algorithms, and observed outcome data.
  • The package's performance was validated by replicating analyses from previous studies using proprietary software, focusing on valproate and canagliflozin safety.

Main Results:

  • The R package successfully replicated findings from previous TBSS analyses, demonstrating its accuracy and reliability.
  • The software provides a reproducible and accessible alternative to proprietary tools for TBSS.

Conclusions:

  • The open-source R package democratizes TBSS methods, promoting innovation and collaboration in pharmacovigilance.
  • The package offers an intuitive interface and extensible object-oriented design, empowering both new and expert users to conduct and advance TBSS analyses.