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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...
Blinding01:11

Blinding

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.
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Hazard Ratio01:12

Hazard Ratio

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.
For example, in a clinical trial evaluating a...

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Updated: May 21, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Bayesian clinical trials in action.

J Jack Lee1, Caleb T Chu

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, U.S.A. jjlee@mdanderson.org

Statistics in Medicine
|June 20, 2012
PubMed
Summary
This summary is machine-generated.

Bayesian methods offer advantages for clinical trial design, commonly used in dose finding and monitoring. These advanced statistical approaches can lead to more efficient trials and better patient outcomes.

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An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

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Last Updated: May 21, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Statistics
  • Clinical Trial Design
  • Biostatistics

Background:

  • The frequentist paradigm has dominated clinical trial design since the 1940s but has limitations.
  • Advancements in computational power have significantly improved the Bayesian paradigm as an alternative.
  • Bayesian methods offer unique advantages and are increasingly incorporated into clinical trials.

Purpose of the Study:

  • To review the literature and assess the applications of Bayesian methods in clinical trials.
  • To identify common uses and infrastructure requirements for Bayesian clinical trial implementation.
  • To discuss the convergence of Bayesian and frequentist approaches in statistical practice.

Main Methods:

  • Extensive literature review to identify Bayesian method applications in clinical trials.
  • Analysis of common use cases including dose finding, efficacy/toxicity monitoring, and PK/PD studies.
  • Examination of infrastructure needs such as specialized software and web applications.

Main Results:

  • Bayesian methods are most frequently applied in dose finding, efficacy/toxicity monitoring, diagnosis, and PK/PD studies.
  • Implementation requires specialized software for design, simulation, analysis, and web-based applications for data management.
  • Evidence suggests a practical convergence between Bayesian and frequentist approaches.

Conclusions:

  • Bayesian methods provide attractive alternatives for improving clinical trial efficiency, reducing sample sizes, and enhancing accuracy.
  • Successful implementation necessitates proper tools, timely data management, quality control, and adaptive randomization.
  • Further design and conduct of Bayesian trials are recommended to demonstrate their benefits in practice.