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Variance01:15

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The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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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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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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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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Variance prior specification for a basket trial design using Bayesian hierarchical modeling

Kristen M Cunanan1, Alexia Iasonos1, Ronglai Shen1

  • 1Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Clinical Trials (London, England)
|December 12, 2018
PubMed
Summary

For oncology basket trials, Bayesian hierarchical modeling is common. Simulations show inverse-gamma priors are sensitive, while uniform or half-t priors offer robust performance for sharing information across patient groups.

Keywords:
Basket trialBayesian methodadaptive designphase IIvariance prior

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Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Targeted therapies are revolutionizing oncology, leading to adaptive clinical trial designs like basket trials.
  • Basket trials enroll patients across multiple diseases based on genomic mutations, forming distinct cohorts or 'baskets'.
  • Bayesian hierarchical modeling is frequently used to analyze correlated endpoints in basket trials, requiring careful prior specification for variance parameters.

Purpose of the Study:

  • To evaluate Bayesian adaptive designs for non-randomized basket trials.
  • To compare the performance of different prior specifications for the variance parameter in Bayesian hierarchical models.
  • To provide recommendations for selecting appropriate priors to ensure robust operating characteristics.

Main Methods:

  • The study employed Bayesian adaptive designs within a non-randomized basket trial framework.
  • Three common prior specifications were investigated: inverse-gamma on variance, and uniform and half-t on standard deviation.
  • Simulation studies were conducted to assess the operating characteristics of these priors under various scenarios.

Main Results:

  • The inverse-gamma prior demonstrated high sensitivity to hyperparameters, potentially leading to unacceptable false-positive rates when the prior mean is near zero.
  • Priors like the uniform and half-t, which place sufficient mass in the tail, exhibited desirable and robust operating characteristics.
  • The effectiveness of uniform and half-t priors is contingent on setting appropriate bounds (upper bound > 1 for uniform, scale parameter > 1 for half-t).

Conclusions:

  • Priors concentrating density near zero for the variance parameter should be avoided in hierarchical models for basket trials.
  • Such priors can force information sharing irrespective of true basket efficacy, a drawback of many inverse-gamma specifications.
  • The uniform or half-t priors on the standard deviation are recommended for their robustness and desirable performance in basket trial design.