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

Randomized Experiments01:13

Randomized Experiments

9.3K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.3K
Blinding01:11

Blinding

4.0K
Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
4.0K
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

367
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
367
Crossover Experiments01:16

Crossover Experiments

4.7K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
4.7K
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

354
Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
354
Blind Procedures02:07

Blind Procedures

13.8K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
13.8K

You might also read

Related Articles

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

Sort by
Same author

Ultra-Processed Foods and Gastrointestinal Cancer: Epidemiologic Evidence, Mechanistic Pathways, and Clinical Implications.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Site- and age-dependent associations between Fusobacterium nucleatum and colorectal cancer mortality.

Cancer·2026
Same author

Abundance and balance of circulating leukocyte subsets and colorectal cancer survival.

British journal of cancer·2026
Same author

Distinct Symptom Occurrence Subgroups Among Patients With Colorectal Cancer: Differences in Social Determinants of Health and Diet Quality.

Research in nursing & health·2026
Same author

The CHRONO trial: Protocol for a randomized controlled trial of early time-restricted eating in patients with breast or rectal cancer.

Nutrition research (New York, N.Y.)·2026
Same author

No-cost envelope modification improves fecal immunochemical test laboratory acceptance in a US Veterans Affairs colorectal cancer screening trial.

Journal of medical screening·2026

Related Experiment Video

Updated: Mar 17, 2026

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
04:53

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

11.4K

Randomized controlled trials: who fails run-in?

Judy R Rees1, Leila A Mott1, Elizabeth L Barry1

  • 1Department of Epidemiology, Geisel School of Medicine at Dartmouth, HB 7927, Dartmouth-Hitchcock Medical Center 1 Medical Center Drive, Lebanon, NH, 03756, USA.

Trials
|July 31, 2016
PubMed
Summary

Predicting run-in failure (RIF) in clinical trials is key to improving efficiency. Factors like age, education, and health status influence RIF risk, highlighting opportunities for targeted interventions to enhance participant retention.

Keywords:
AdherenceGeneralizabilityRandomized controlled trialsRun-in

More Related Videos

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

12.8K
Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health
06:13

Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health

Published on: December 1, 2023

1.8K

Related Experiment Videos

Last Updated: Mar 17, 2026

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
04:53

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition

Published on: September 20, 2019

11.4K
A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

12.8K
Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health
06:13

Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health

Published on: December 1, 2023

1.8K

Area of Science:

  • Clinical trial methodology
  • Preventive medicine
  • Colorectal cancer research

Background:

  • Run-in failure (RIF) can decrease clinical trial efficiency and generalizability.
  • Identifying participants at risk of RIF early can mitigate these issues.
  • This study investigated baseline predictors of RIF in a colorectal adenoma recurrence trial.

Purpose of the Study:

  • To identify baseline factors associated with run-in failure (RIF) in a randomized controlled trial.
  • To explore differences in RIF predictors between men and women, and across different randomization arms.
  • To inform strategies for improving participant retention and trial efficiency.

Main Methods:

  • A partial factorial-design, randomized controlled trial of calcium and vitamin D for colorectal adenoma recurrence.
  • Baseline data collected via self-administered questionnaires (SAQs) and staff-administered questionnaires.
  • Logistic regression models used to analyze RIF predictors in three subgroups: men, women in full factorial randomization, and women in two-group randomization.

Main Results:

  • Overall RIF rate was 12%, with poor adherence or withdrawal/uncooperativeness as primary reasons.
  • In men, RIF decreased with age and was associated with single status, lower education, and missing SAQ data.
  • In women, RIF was linked to poorer physical health, no multivitamin use, longer surveillance intervals, and more prescription medications; perceived toxicities significantly increased RIF odds.

Conclusions:

  • Few common baseline predictors of RIF were identified across all groups.
  • Significant heterogeneity in RIF by study center and missing SAQ data suggest areas for intervention.
  • Addressing study center variations and improving data completion can enhance trial efficiency and participant retention.