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Related Concept Videos

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...
Blinding01:11

Blinding

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.
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Experimental Designs01:16

Experimental Designs

An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...

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Related Experiment Video

Updated: May 20, 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

Adaptive Bayesian randomized trials: realizing their potential.

Eleanor M Pullenayegum1

  • 1Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada. pullena@mcmaster.ca

The Journal of Bone and Joint Surgery. American Volume
|July 20, 2012
PubMed
Summary

Bayesian adaptive designs allow clinical trials to change based on new data, which is useful for rapidly evolving interventions. These flexible designs, including arm dropping and sample size adjustments, can be tailored for various trial types.

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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Last Updated: May 20, 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

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

Area of Science:

  • Clinical Trial Methodology
  • Biostatistics
  • Medical Research

Background:

  • Clinical trials often face challenges with rapidly evolving interventions.
  • Traditional trial designs lack flexibility to incorporate accumulating evidence.
  • Bayesian adaptive designs offer a solution to enhance trial efficiency and relevance.

Purpose of the Study:

  • To explore the application and benefits of Bayesian adaptive designs in clinical research.
  • To demonstrate how adaptive designs can be tailored to specific trial objectives.
  • To provide examples of adaptive designs suitable for different research phases and goals.

Main Methods:

  • Utilizing Bayesian statistical principles for trial design and analysis.
  • Implementing adaptive elements such as early stopping, sample size re-estimation, and arm dropping.
  • Matching adaptive design features to distinct trial types: exploratory, explanatory, and cost-effectiveness.

Main Results:

  • Bayesian adaptive designs provide significant flexibility in trial conduct.
  • Adaptations can be implemented based on interim data analysis.
  • The choice of adaptive strategy is crucial and depends on the trial's objectives.

Conclusions:

  • Bayesian adaptive designs are valuable for studying evolving interventions.
  • Flexible trial designs improve efficiency and ethical considerations.
  • Appropriate selection of adaptive designs ensures alignment with research goals, from exploration to cost-effectiveness analysis.