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

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

803
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...
803
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

550
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
550
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

302
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
302
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

7.6K
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...
7.6K
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

367
SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
367
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

725
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
725
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Data Management And Data Science
  9. Query Processing And Optimisation
  11. Framework For Exploration Of Statistical Heterogeneity In Multi-database Studies: A Case Study Using Exacos-cv Studies.
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Data Management And Data Science
  9. Query Processing And Optimisation
  11. Framework For Exploration Of Statistical Heterogeneity In Multi-database Studies: A Case Study Using Exacos-cv Studies.

Related Experiment Video

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.7K

Framework for Exploration of Statistical Heterogeneity in Multi-Database Studies: A Case Study Using EXACOS-CV Studies.

Kirsty Marie Rhodes1, Edeltraut Garbe2, Hana Müllerová3

  • 1Real-World Science and Analytics, BioPharmaceuticals Medical, AstraZeneca, Cambridge, UK.

Clinical Epidemiology
|June 19, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Multi-database studies reveal significant variation in outcomes, but a new framework helps analyze these differences. This approach distinguishes methodological issues from true population variations, improving interpretation of complex health data.

Keywords:
cardiovascular outcomeschronic obstructive pulmonary diseaseexacerbationheterogeneity

More Related Videos

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.1K
Development of Compendium for Esophageal Squamous Cell Carcinoma
03:36

Development of Compendium for Esophageal Squamous Cell Carcinoma

Published on: April 12, 2024

534

Related Experiment Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.7K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.1K
Development of Compendium for Esophageal Squamous Cell Carcinoma
03:36

Development of Compendium for Esophageal Squamous Cell Carcinoma

Published on: April 12, 2024

534

Area of Science:

  • Epidemiology
  • Health Services Research
  • Biostatistics

Background:

  • Multi-database studies offer valuable insights but often yield heterogeneous results, complicating interpretation.
  • Understanding the sources of this statistical heterogeneity is crucial for accurate conclusions in large-scale health research.

Purpose of the Study:

  • To develop a systematic framework for identifying and assessing sources of statistical heterogeneity in multi-database studies.
  • To apply this framework to the EXAcerbations of COPD and their OutcomeS on CardioVascular diseases (EXACOS-CV) program.

Main Methods:

  • Developed a conceptual framework distinguishing between methodological diversity (study design, database selection) and true clinical variation (population, healthcare differences).
  • Utilized a novel checklist to identify and explore sources of methodological diversity.
multi-database study
real-world data
  • Applied the framework and checklist to EXACOS-CV cohort studies in Germany, Canada, Netherlands, and Spain, focusing on adjusted hazard ratios (aHR) for heart failure and all-cause death post-exacerbation.

    • Main Results:

    • Significant heterogeneity was observed in aHR for heart failure (ranging from 2.6 in Germany to 72.3 in Canada) and all-cause death (3.5 in Netherlands to 22.1 in Spain) post-exacerbation.
    • Methodological diversity, including variations in capturing cardiovascular comorbidities and measuring confounders, partially explained the heterogeneity.
    • Standardizing models did not fully resolve heterogeneity, suggesting genuine population-level variations in cardiovascular disease prevalence contribute.

    Conclusions:

    • A developed framework effectively aids in assessing sources of heterogeneity in multi-database studies.
    • Multi-database research can yield directional insights while accounting for population and healthcare system differences.
    • Genuine variations in cardiovascular disease prevalence may significantly influence study outcomes.