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

405
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...
405
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

447
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
447
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

185
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
185
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

132
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...
132
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

133
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
133
Clinical Trials01:16

Clinical Trials

9.3K
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...
9.3K

You might also read

Related Articles

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

Sort by
Same author

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same author

Methodological Approaches to Real-World Evidence Generation for Glucagon-like Peptide-1-Based Therapies: Synopsis of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

Annals of internal medicine·2026
Same author

Leveraging Real-World Evidence to Inform Regulatory, Clinical, and Coverage Decisions Related to Glucagon-Like Peptide-1-Based Therapies: Synopsis of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

Annals of internal medicine·2026
Same author

PEPRVision: a visualization framework for Pharmacoepidemiology research.

American journal of epidemiology·2026
Same author

Health Care Costs and Use of Patients Prescribed Four Different Obesity Medications.

Obesity (Silver Spring, Md.)·2026
Same author

Sequential Safety Surveillance of RSVpreF Vaccination During Pregnancy Early in the Postapproval Period.

JAMA network open·2026

Related Experiment Video

Updated: Sep 22, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

9.7K

Invited Commentary: Go BIG and Go Global-Executing Large-Scale, Multisite Pharmacoepidemiologic Studies Using

Judith C Maro, Sengwee Toh

    American Journal of Epidemiology
    |May 21, 2022
    PubMed
    Summary

    Postmarket studies require large patient records for safety and effectiveness. Observational database studies using distributed data networks and common data models are practical for generating real-world evidence.

    Keywords:
    common data modelmultisite studiesnetworkspharmacoepidemiologyreal-world data

    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

    14.7K
    High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry
    10:17

    High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry

    Published on: April 23, 2019

    9.8K

    Related Experiment Videos

    Last Updated: Sep 22, 2025

    Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
    05:10

    Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

    Published on: December 11, 2016

    9.7K
    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

    14.7K
    High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry
    10:17

    High-throughput and Comprehensive Drug Surveillance Using Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry

    Published on: April 23, 2019

    9.8K

    Area of Science:

    • Pharmacoepidemiology
    • Health Informatics
    • Real-World Evidence Generation

    Background:

    • Limited knowledge of comparative safety and effectiveness of medical products at approval.
    • Need for postmarket evidence on rare adverse events requires large-scale longitudinal patient data analysis.

    Purpose of the Study:

    • To demonstrate the utility of observational database studies for postmarket evidence generation.
    • To highlight the importance of selecting "fit for purpose" databases.

    Main Methods:

    • Utilizing distributed data networks with common data models.
    • Standardized and structured data capture across disparate real-world data sources.
    • Employing reproducible, standardized programming approaches for transparency.

    Main Results:

    • Distributed data networks facilitate efficient capture of patient data.
    • Common data models enable transparency and reproducibility.
    • The architecture supports multisite observational studies and pragmatic clinical trials.

    Conclusions:

    • Observational database studies, when "fit for purpose," are practical for postmarket surveillance.
    • Distributed data networks and common data models enhance the scale, diversity, and international reach of studies.
    • These methods improve the generation of real-world evidence for patient and provider guidance.