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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...
Random Sampling Method01:09

Random 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. Data are the result of sampling from a 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. Among the various sampling methods used by...
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Bias01:22

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

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Related Experiment Video

Updated: Jul 16, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Power and sample size simulations for Randomized Play-the-Winner rules.

Paulo Guimaraes1, Yuko Palesch

  • 1Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, 135 Cannon St. Suite 303, Charleston, SC 29425, USA. guimarap@musc.edu

Contemporary Clinical Trials
|February 27, 2007
PubMed
Summary

Response-adaptive randomization, like Randomized Play-the-Winner (RPW), ethically favors better treatments in clinical trials. This study provides crucial sample size and power assessments for implementing RPW with binary outcomes.

Related Experiment Videos

Last Updated: Jul 16, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Ethical Research Design

Background:

  • Response-adaptive randomization procedures aim to ethically allocate more patients to better-performing treatments during a clinical trial.
  • The Randomized Play-the-Winner (RPW) is a prominent example of such adaptive methods.
  • Practical implementation of RPW faces challenges, particularly in estimating required sample size and predicting treatment allocation proportions.

Purpose of the Study:

  • To provide simulation-based assessments of statistical power and sample size requirements for the RPW rule.
  • To address the practical difficulties in determining sample size for RPW in binary outcome trials.
  • To discuss feasible approaches for sample size determination when using RPW.

Main Methods:

  • Utilized simulation studies to evaluate the performance of the RPW rule.
  • Focused on clinical trials with a primary outcome variable that is binary.
  • Analyzed the relationship between sample size, statistical power, and allocation probabilities under RPW.

Main Results:

  • Simulation results offer a realistic assessment of power and sample size needs for RPW implementation.
  • Quantified the expected allocation shares for treatments under RPW.
  • Demonstrated the feasibility of determining sample size using simulation-based approaches for RPW.

Conclusions:

  • RPW offers an ethical advantage by favoring superior treatments.
  • Accurate sample size and power estimations are critical for successful RPW implementation.
  • Simulation-based methods provide practical solutions for sample size determination in RPW-guided trials.