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Related Concept Videos

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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Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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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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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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In Silico Clinical Trials for Cardiovascular Disease
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Sample size determination in clinical trials with multiple co-primary endpoints including mixed continuous and binary

Takashi Sozu1, Tomoyuki Sugimoto, Toshimitsu Hamasaki

  • 1Department of Biostatistics, Kyoto University School of Public Health, Yoshida Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan. sozu.takashi.4s@kyoto-u.ac.jp

Biometrical Journal. Biometrische Zeitschrift
|July 26, 2012
PubMed
Summary

Determining sample size for clinical trials with multiple co-primary endpoints is crucial. Higher correlation between endpoints reduces the required sample size while maintaining desired statistical power.

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Area of Science:

  • Pharmaceutical Drug Development
  • Clinical Trial Design
  • Biostatistics

Background:

  • Establishing statistically significant efficacy for new treatments often involves multiple co-primary endpoints.
  • Increasing the number of co-primary endpoints elevates the type II error rate, necessitating careful sample size determination.
  • Preserving desired overall power is critical when designing trials with multiple co-primary endpoints.

Purpose of the Study:

  • To address the challenge of sample size determination for clinical trials with multiple co-primary endpoints.
  • To investigate overall power functions and sample size calculations for mixed continuous and binary endpoints.
  • To provide numerical examples illustrating the behavior of power functions and sample sizes.

Main Methods:

  • The study considers overall power functions and sample size determinations for multiple co-primary endpoints.
  • The methodology accommodates mixed continuous and binary variables.
  • Response variables are modeled using a multivariate normal distribution, with binary variables derived from a dichotomized normal distribution.

Main Results:

  • Numerical examples demonstrate the relationship between correlation and sample size.
  • The study shows that increased correlation between endpoints leads to a decrease in the required sample size.
  • This effect is observed when individual endpoint powers are approximately equal.

Conclusions:

  • Accurate sample size calculation is vital for clinical trials with multiple co-primary endpoints.
  • The correlation between endpoints significantly impacts sample size requirements.
  • Understanding these relationships aids in efficient clinical trial design and resource allocation.