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Related Concept Videos

Goodness-of-Fit Test01:16

Goodness-of-Fit Test

The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
Introduction to Test of Independence01:21

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
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.
Flow Table Test01:12

Flow Table Test

The flow table test is an established method used to assess the workability of concrete, particularly useful for evaluating highly flowable concrete mixes. This test employs an apparatus that consists of a wooden board topped with a steel plate, collectively weighing 35 pounds. The board is connected to a base via a hinge and measures 27.6 inches on each side.
Concrete is placed within a truncated cone mold that is 8 inches high with an 8-inch base diameter and a 5-inch top diameter. The...
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

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Related Experiment Video

Updated: Jun 24, 2026

Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation
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Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation

Published on: March 8, 2018

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Adaptive Platform Trials in Stroke.

Elizabeth Lorenzi1, Amy M Crawford1, Craig S Anderson2,3

  • 1Berry Consultants LLC, Austin, TX (E.L., A.M.C., S.M.B., R.J.L.).

Stroke
|December 20, 2024
PubMed
Summary
This summary is machine-generated.

Platform trials accelerate stroke treatment research by evaluating multiple interventions simultaneously. This approach enhances efficiency and speeds up the delivery of life-saving therapies to stroke patients.

Keywords:
Bayesian analysisadaptive clinical trialsrandomized control trialsresearch designstroke

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Area of Science:

  • Neurology
  • Clinical Trials Methodology
  • Biostatistics

Background:

  • Traditional stroke clinical trials use resource-intensive, single-intervention designs.
  • Rapidly evolving stroke treatments necessitate more efficient research methods.

Purpose of the Study:

  • To introduce adaptive platform trials as a solution for accelerating stroke research.
  • To explore the design, implementation, and statistical considerations of these trials.

Main Methods:

  • Utilizing a master protocol to evaluate multiple interventions within a single trial infrastructure.
  • Employing multifactorial designs, Bayesian modeling, and adaptive features.
  • Highlighting two new platform trials: STEP and ACT-GLOBAL for acute ischemic stroke.

Main Results:

  • Platform trials maximize information per participant.
  • They align research with clinical care complexities.
  • These trials accelerate the identification of effective stroke therapies.

Conclusions:

  • Adaptive platform trials offer a powerful framework to expedite the discovery of new stroke treatments.
  • They hold significant potential for improving stroke management and rehabilitation.