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Related Concept Videos

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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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,...
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Clinical Trials01:16

Clinical Trials

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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...
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Clinical Trials: Overview01:11

Clinical Trials: Overview

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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...
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Blinding01:11

Blinding

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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.
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Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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Crossover Experiments01:16

Crossover Experiments

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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: Sep 14, 2025

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

Amy LaLonde1, Steven E Nissen2

  • 1Eli Lilly and Company, Indianapolis.

NEJM Evidence
|July 22, 2025
PubMed
Summary

Multiple hypothesis testing risks false discoveries. This review explains traditional methods and graphical testing to control statistical significance errors for nonstatisticians.

Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Trials

Background:

  • Multiple statistical hypothesis testing increases the risk of false discoveries (Type I errors).
  • Nonstatisticians often face challenges in understanding and applying appropriate statistical methods.
  • Accurate interpretation of study results is crucial in scientific research.

Purpose of the Study:

  • To introduce nonstatisticians to methods for controlling the rate of false discoveries.
  • To explain traditional approaches for managing Type I error rates.
  • To provide an accessible overview of graphical testing procedures.

Main Methods:

  • Review of traditional statistical methods for multiple hypothesis testing.
  • Explanation of the principles behind controlling Type I error.

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  • Introduction to the graphical testing approach.

    • Main Results:

    • Traditional methods offer ways to manage the increased risk of false positives.
    • Graphical testing provides a structured procedure to maintain statistical validity.
    • Understanding these methods helps prevent erroneous conclusions from data.

    Conclusions:

    • Implementing appropriate statistical controls is essential when testing multiple hypotheses.
    • Graphical testing is a valuable, albeit seemingly complex, tool for ensuring result validity.
    • This review aims to demystify these concepts for broader accessibility in research.