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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...
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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...
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.
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...

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Related Experiment Video

Updated: May 13, 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

Randomization in clinical trials: stratification or minimization? The HERMES free simulation software.

Hélène Fron Chabouis1, Francis Chabouis, Florence Gillaizeau

  • 1Sorbonne Paris Cité, Faculté de chirurgie dentaire, Biomaterials department (URB2i, EA4462), Clinical Research Unit, Université Paris Descartes, 1 rue Maurice Arnoux, Montrouge, 92120, France, helene.fron@parisdescartes.fr.

Clinical Oral Investigations
|March 5, 2013
PubMed
Summary

Minimization is superior to stratification in complex clinical trials, offering better balance and predictability. This study introduces HERMES software to aid investigators in selecting the optimal randomization method for two-arm trials.

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Related Experiment Videos

Last Updated: May 13, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

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Published on: January 8, 2020

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

  • Clinical trial methodology
  • Biostatistics
  • Randomization techniques

Background:

  • Operative clinical trials are frequently small and open-label, necessitating robust randomization.
  • Stratification and minimization are key randomization methods for ensuring treatment group balance.

Purpose of the Study:

  • To compare stratification and minimization methods regarding predictability and balance in clinical trials.
  • To evaluate the impact of various parameters on the performance of these randomization techniques.
  • To provide investigators with tools to select the most appropriate allocation method.

Main Methods:

  • Developed software (HERMES) to simulate patient allocation based on trial parameters.
  • Calculated predictability and balance indicators for stratification and minimization over 10,000 simulations.
  • Modeled a reference trial and eight derived trials with varying parameters.

Main Results:

  • Minimization demonstrated superior performance in complex trials (smaller sample size, more prognostic factors/operators).
  • Stratification imbalance increased with a higher number of strata.
  • An inverse correlation was observed between imbalance and predictability.

Conclusions:

  • Investigators must balance predictability and imbalance when choosing randomization methods.
  • The HERMES software offers data-driven guidance for selecting between stratification and minimization.
  • This tool aims to improve randomization strategies in future two-arm clinical trials.