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

Quality Assurance01:19

Quality Assurance

4.0K
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
4.0K
Quality Control01:05

Quality Control

4.2K
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
4.2K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

627
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,...
627
Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

831
Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
831
Response Surface Methodology01:16

Response Surface Methodology

904
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
904
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

391
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
391

You might also read

Related Articles

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

Sort by
Same author

Real-World Diagnostic Workup of Patients Suspected for Light Chain Amyloidosis and Wild-Type Transthyretin Amyloid Cardiomyopathy: A Retrospective Cohort Study Using US Electronic Health Records.

EJHaem·2026
Same author

Correction: Incremental Disease Burden (Healthcare Costs and Resources) of Duchenne Muscular Dystrophy in the US: A Matched Cohort Analysis.

PharmacoEconomics - open·2026
Same author

Methodological and regulatory considerations for causal AI in drug development.

NPJ digital medicine·2026
Same author

A critical assessment of matching-adjusted indirect comparisons in relation to target populations.

Research synthesis methods·2026
Same author

Novel approaches for random-effects meta-analysis of a small number of studies under normality.

Research synthesis methods·2026
Same author

Incremental Disease Burden (Healthcare Costs and Resources) of Duchenne Muscular Dystrophy in the US: A Matched Cohort Analysis.

PharmacoEconomics - open·2026
Same journal

Cross-tool evaluation of artificial intelligence-drafted informed consent documents: A 3-level study.

Perspectives in clinical research·2026
Same journal

Preparing for central drugs standard control organization ethics committee inspections in India: A review of regulatory expectations and readiness strategies.

Perspectives in clinical research·2026
Same journal

Competencies and operations of research ethics committee members and the protection of research participants: A scoping review.

Perspectives in clinical research·2026
Same journal

The Consolidated Standards of Reporting Trials Statement-2025: New epoch for improving the transparency of randomized trials reporting.

Perspectives in clinical research·2026
Same journal

Cost analysis and drug utilization pattern in diabetic patients attending outpatient at tertiary care teaching hospital in South Gujarat.

Perspectives in clinical research·2026
Same journal

Qualitative research - Part 1.

Perspectives in clinical research·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

A data-driven approach to quality risk management.

Demissie Alemayehu1, Jose Alvir, Marcia Levenstein

  • 1Specialty Care Business Unit, Clinical Affairs, Statistics (DA, JA, ML) and Worldwide Research & Development, Clinical Quality Management (DN), Pfizer, Inc., New York, USA.

Perspectives in Clinical Research
|December 7, 2013
PubMed
Summary
This summary is machine-generated.

A new integrated strategy for clinical trial management identifies key risk factors predicting quality issues. This approach optimizes resource use and ensures patient safety and trial integrity.

Keywords:
Clinical trialcompliancequality risk managementrisk assessment and mitigation

Related Experiment Videos

Last Updated: May 5, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Area of Science:

  • Clinical trial management
  • Pharmaceutical research
  • Quality assurance in research

Background:

  • Ensuring patient safety, trial quality, and efficiency is paramount in clinical research.
  • An integrated approach to clinical trial strategy involves risk factor identification, mitigation, and real-time quality assessment.
  • Data-driven techniques can enhance quality management by identifying critical risk factors for trial issues.

Purpose of the Study:

  • To illustrate a data-driven approach for identifying risk factors impacting clinical trial quality.
  • To demonstrate how an integrated quality management plan can be enhanced using real-world trial data.
  • To apply statistical methods to actual clinical trial data to predict quality issues.

Main Methods:

  • Utilized statistical methods including Wilcoxon rank-sum test and logistic regression.
  • Analyzed data from clinical trials sponsored by Pfizer to identify risk factor associations.
  • Focused on the relationship between identified risk factors and the occurrence of quality issues.

Main Results:

  • A subset of risk factors showed a significant association with clinical trial quality issues.
  • Identified risk factors include: use of placebo, biologic agents, unusual packaging, complex dosing, and over 25 planned procedures.
  • These specific factors are predictive of potential quality deviations in clinical trials.

Conclusions:

  • Implementing this integrated strategy optimizes resource utilization in clinical trials.
  • The approach effectively enhances trial integrity and upholds patient safety.
  • Data-driven risk factor identification is crucial for proactive quality management in clinical research.