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

Cancer Survival Analysis01:21

Cancer Survival Analysis

508
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
508
Factors Affecting Protein-Drug Binding: Patient-Related Factors01:29

Factors Affecting Protein-Drug Binding: Patient-Related Factors

173
Protein-drug binding, a pivotal aspect of pharmacokinetics, is subject to considerable variability influenced by an array of patient-related factors. The intricate interplay of age, individual differences, and pathological conditions significantly impact the binding dynamics and subsequent pharmacological effects.
Age stands as a key determinant in protein-drug binding. Neonates, characterized by low albumin content, experience heightened concentrations of unbound drugs such as phenytoin and...
173
Factors Affecting Drug Response: Overview01:21

Factors Affecting Drug Response: Overview

2.6K
When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
2.6K
Hazard Ratio01:12

Hazard Ratio

353
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
353
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

236
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,...
236
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

387
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...
387

You might also read

Related Articles

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

Sort by
Same author

Metformin modifies hormone changes associated with androgen deprivation therapy for prostate cancer.

Endocrine oncology (Bristol, England)·2026
Same author

From framework to practice: applying the proteus-practice framework to learn lessons for integrating PROs in clinical care.

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation·2026
Same author

Structured Exercise Program After Adjuvant Chemotherapy for Colon Cancer: A Cost-Utility Analysis of the CHALLENGE Trial.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

Clinical and molecular correlates of circulating tumor fraction in patients with metastatic pancreatic ductal adenocarcinoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Establishment of harmonized international reference ranges for plasma estradiol concentrations in postmenopausal women.

The Journal of clinical endocrinology and metabolism·2026
Same author

Occupational exposure to endocrine-disrupting chemicals and colorectal cancer risk - An analysis of four participating cohorts of the Canadian Partnership for Tomorrow's Health study.

Environmental research·2026

Related Experiment Video

Updated: Nov 10, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

485

Factors predicting missing instruments in three cancer randomized clinical trials.

Michael J Palmer1,2,3, Harriet Richardson4,5,6, Dongsheng Tu4,6

  • 1Department of Public Health Sciences, Queen's University, Kingston, ON, Canada. 81mjp6@queensu.ca.

Quality of Life Research : an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation
|April 2, 2021
PubMed
Summary

Identifying factors predicting missing patient-reported outcome data in randomized clinical trials is crucial. Few factors, like baseline age and center characteristics, significantly predicted missing instruments in this study.

Keywords:
CancerFactorsMissing instrumentsMissing patient-reported outcome dataRandomized clinical trials

More Related Videos

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

7.7K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

314

Related Experiment Videos

Last Updated: Nov 10, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

485
Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

7.7K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

314

Area of Science:

  • Clinical Trials
  • Biostatistics
  • Health Services Research

Background:

  • Missing patient-reported outcome (PRO) data compromises the integrity of randomized clinical trials (RCTs).
  • Understanding predictors of missing PRO data is essential for developing effective prevention strategies.
  • Previous research suggests various factors may influence data completeness in clinical trials.

Purpose of the Study:

  • To investigate the association of multiple factors with the time to the first missing instrument after randomization in three cooperative group RCTs.
  • To identify key predictors that can inform strategies to minimize missing PRO data in future trials.
  • To examine these associations across different cancer types and trial settings.

Main Methods:

  • Descriptive analyses and Cox proportional hazards regressions were conducted on data from three Canadian Cancer Trials Group RCTs (MA17, PR7, LY12).
  • The primary outcome was the time from randomization to the first missing instrument.
  • Fifteen potential factors were analyzed as covariates, selected based on availability and prior evidence.

Main Results:

  • Missing instrument rates varied significantly across trials: 9% (MA17), 37% (PR7), and 59% (LY12).
  • Median time to the first missing instrument ranged from 0.12 years (LY12) to not observed (MA17).
  • Statistically significant predictors included baseline age, level of well-being, center activity level, postgraduate training presence, and center geographic location.

Conclusions:

  • Many commonly cited factors do not appear to predict the time to the first missing instrument in RCTs.
  • The few significant predictors identified suggest that context-specific factors are important.
  • Further research is needed to understand the nuances of missing data in different trial settings.