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

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

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...
Randomized Experiments01:13

Randomized Experiments

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...
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

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...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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, controlled...
Crossover Experiments01:16

Crossover Experiments

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.
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

You might also read

Related Articles

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

Sort by
Same author

Placental growth factor (PLGF)-based testing to help diagnose suspected pre-eclampsia: a systematic review and economic evaluation.

Health technology assessment (Winchester, England)·2026
Same author

Expert opinion in decision-making: a systematic review of methods and the INTEGRITY framework for incorporating expert consultation into research.

Research integrity and peer review·2026
Same author

Evaluating the Impact of Different Natural History Modeling Methods on Cost-Effectiveness Decisions: A Case Study in Duchenne Muscular Dystrophy.

MDM policy & practice·2026
Same author

Variation in Tier 3 Specialist Weight Management Services Within the National Health Service (NHS): Insights From Four NHS Sites in England and Wales.

Clinical obesity·2026
Same author

Estimating the impact of cancer diagnosis on life expectancy by stage at diagnosis: population-based estimates for a range of cancer sites in England.

BMJ oncology·2026
Same author

Mapping the Use of Real-World Evidence Across the EU Health Technology Assessment Regulation: Methodological Considerations, Challenges, and Opportunities for Harmonization.

Journal of market access & health policy·2026

Related Experiment Video

Updated: Jun 5, 2026

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

Assessing methods for dealing with treatment switching in randomised controlled trials: a simulation study.

James P Morden1, Paul C Lambert, Nicholas Latimer

  • 1ICR-CTSU, The Institute of Cancer Research, Cotswold Road, Sutton, Surrey, UK. James.Morden@icr.ac.uk

BMC Medical Research Methodology
|January 13, 2011
PubMed
Summary

Simple methods for analyzing clinical trial survival outcomes with treatment switching can introduce bias. The Branson & Whitehead method offers a less biased alternative for estimating true treatment effects in these complex scenarios.

Related Experiment Videos

Last Updated: Jun 5, 2026

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

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Survival Analysis

Background:

  • Clinical trials with survival outcomes often involve patients switching treatments.
  • Simple analysis methods may introduce selection bias if switchers differ from the general patient population.
  • Advanced methods adjusting for treatment switching were also examined.

Purpose of the Study:

  • To evaluate methods for analyzing clinical trial survival data when treatment switching occurs.
  • To compare the performance of simple versus adjusted methods under various switching scenarios.

Main Methods:

  • A simulation study was designed to assess different analytical approaches.
  • 16 distinct scenarios were simulated, varying switching proportions, prognosis, and treatment effect size.
  • 1000 datasets were generated for each scenario to test all methods.

Main Results:

  • Simple methods exhibited selection bias when survival differences between switchers and non-switchers were substantial.
  • The Accelerated Failure Time (AFT) method, specifically the Branson & Whitehead approach, demonstrated reduced bias.
  • The performance of methods varied depending on the simulated scenario parameters.

Conclusions:

  • Standard, simple methods are frequently inadequate for handling treatment switching in survival analyses.
  • Alternative methods, such as the Branson & Whitehead approach, are recommended for adjusting treatment switching.
  • Careful consideration of analytical methods is crucial for accurate treatment effect estimation in trials with switching.