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Related Concept Videos

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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.
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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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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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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...
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In Silico Clinical Trials for Cardiovascular Disease
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An optimised multi-arm multi-stage clinical trial design for unknown variance.

Michael J Grayling1, James M S Wason2, Adrian P Mander1

  • 1Hub for Trials Methodology Research, MRC Biostatistics Unit, Cambridge, UK.

Contemporary Clinical Trials
|February 24, 2018
PubMed
Summary

This study introduces an efficient Monte Carlo simulation method for optimizing multi-arm multi-stage clinical trial designs with unknown variance. The new approach ensures familywise error rate and power close to desired levels for drug development.

Keywords:
Familywise error-rateGroup sequentialInterim analysesMulti-arm multi-staget-Statistic

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Area of Science:

  • Clinical Trial Design
  • Biostatistics
  • Drug Development

Background:

  • Multi-arm multi-stage (MAMS) trials enhance drug development efficiency.
  • Standard MAMS designs often assume known patient response variance, which is unrealistic.
  • Previous methods for unknown variance used t-tests and quantile substitution, with mixed results.

Purpose of the Study:

  • To develop and evaluate an alternative method for optimizing MAMS trial designs when patient variance is unknown.
  • To provide a flexible approach for determining group sizes and stopping boundaries in t-test based MAMS trials.
  • To ensure control of the familywise error rate (FWER) and achieve desired power levels.

Main Methods:

  • Utilized Monte Carlo simulation to optimize group sizes and stopping boundaries for MAMS trials.
  • Employed t-test statistics to accommodate unknown patient response variance.
  • Developed and implemented R code for general application of the proposed methodology.

Main Results:

  • The proposed Monte Carlo simulation method effectively optimizes MAMS trial designs.
  • Achieved familywise error rate and power levels close to the nominal levels specified.
  • Demonstrated the practical utility of the method through several examples.

Conclusions:

  • The developed Monte Carlo simulation approach offers a robust solution for MAMS trial design with unknown variance.
  • This methodology enhances the efficiency and reliability of drug development.
  • The provided R code facilitates the implementation of these optimized trial designs.