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Related Concept Videos

Clinical Trials01:16

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
Clinical Trials: Overview01:11

Clinical Trials: Overview

Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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...
Data Collection by Experiments01:13

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...
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...
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.

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Related Experiment Video

Updated: May 9, 2026

Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis
07:40

Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis

Published on: March 20, 2021

Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical

B Ren1, F Ferrari2, S Fortini3

  • 1Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, Massachusetts 02478, U.S.A.

Biometrika
|May 8, 2026
PubMed
Summary

This study introduces a new permutation test using external data to detect if new cancer drugs work in specific patient subgroups. This method increases the power of clinical trials and controls false negatives, improving drug development.

Keywords:
Bayesian statisticsDecision theoryGlioblastomaHeterogeneous treatment effectPermutation test

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Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
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Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

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Related Experiment Videos

Last Updated: May 9, 2026

Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis
07:40

Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis

Published on: March 20, 2021

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
10:27

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Therapeutic efficacy in oncology frequently varies among patient subgroups, posing challenges for early drug development.
  • Randomized clinical trials may yield false negative results when treatment effects are concentrated in subpopulations.
  • External data from clinical studies and electronic health records can enhance decision-making in drug development.

Purpose of the Study:

  • To introduce a novel permutation procedure for testing treatment efficacy in subpopulations using external data.
  • To increase the statistical power of randomized clinical trials when heterogeneous treatment effects are present.
  • To control the false positive rate while accommodating discrepancies between trial and external data.

Main Methods:

  • A permutation procedure is proposed to test the null hypothesis of no treatment effect in any subpopulation.
  • External data is leveraged to enhance the power of the statistical test.
  • The procedure is designed to maintain the desired false positive rate (α) without strict assumptions on external data.

Main Results:

  • The permutation test increases power for detecting heterogeneous treatment effects.
  • The method controls the false positive rate even with unmeasured confounders or differing patient profiles.
  • The test is shown to be optimal and is illustrated with simulations and glioblastoma trial data.

Conclusions:

  • External data can be effectively used with a permutation test to evaluate experimental therapeutics with potential heterogeneous effects.
  • This approach improves the detection of subgroup-specific treatment benefits in oncology.
  • The proposed method offers a robust tool for clinical trial analysis, enhancing drug development decision-making.