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Crossover Experiments

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.
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Factorial Design

Factorial Design

Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...

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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.
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Blinding

Blinding

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Randomized Experiments

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.
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Hazard Ratio

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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
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Related Experiment Video

Updated: Jun 13, 2025

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

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

Published on: September 20, 2019

Design considerations for Factorial Adaptive Multi-Arm Multi-Stage (FAST) clinical trials.

Jonathan Beall¹, Jordan Elm², Matthew W Semler³

¹Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA. bealljo@musc.edu.

|September 11, 2024

Summary

This summary is machine-generated.

Factorial Adaptive Multi-Arm Multi-Stage (FAST) trial designs combine benefits of MAMS and factorial designs. Careful planning of analyses and timing is crucial for optimizing FAST trial operating characteristics.

Keywords:

Adaptive design Clinical trial Factorial design Multi-arm Multi-stage

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Last Updated: Jun 13, 2025

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Area of Science:

Clinical Trial Design
Biostatistics
Pharmaceutical Research

Background:

Multi-Arm, Multi-Stage (MAMS) designs facilitate comparing multiple therapies across trial phases.
Factorial designs combined with MAMS enhance efficiency over traditional fixed designs.
Factorial Adaptive Multi-Arm Multi-Stage (FAST) designs integrate factorial and MAMS approaches.

Purpose of the Study:

To explore design choices for FAST trials.
To assess the impact of analysis type, timing, and effect size on FAST trial operating characteristics.
To evaluate the potential benefits of FAST designs compared to MAMS and factorial designs.

Main Methods:

Simulation studies were employed to analyze FAST designs.
The impact of analysis type, timing, and effect size on trial operating characteristics was assessed.
Analysis approaches varied based on data type for multiple outcomes.

Main Results:

FAST designs offer a framework for potential improvements over MAMS and factorial trials.
Design implementation decisions significantly influence trial operating characteristics.
Simulation results highlight the importance of analysis timing and type.

Conclusions:

FAST trials can potentially offer advantages similar to both MAMS and factorial designs.
Thorough exploration of design aspects during the planning phase is essential for FAST trials.
The chosen design elements critically impact the success and efficiency of FAST trials.