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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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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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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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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between 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:
6.8K

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Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
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Clinical trials with nested subgroups: Analysis, sample size determination and internal pilot studies.

Marius Placzek1, Tim Friede1

  • 1Department of Medical Statistics, University Medical Center Goettingen, Goettingen, Germany.

Statistical Methods in Medical Research
|January 5, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for sample size determination in subgroup analyses for personalized medicine. It ensures statistical power and controls errors, especially in smaller subgroups, using an internal pilot study design.

Keywords:
Subgroup analysisadaptive designinternal pilot studymultiple testingsample size recalculation

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Personalized Medicine

Background:

  • Subgroup analyses are crucial for personalized medicine and targeted therapies.
  • Designing studies with multiple nested subgroups and continuous endpoints presents analytical challenges.
  • Accurate sample size determination is essential for study validity.

Purpose of the Study:

  • To develop robust methods for the analysis and sample size determination in studies with multiple nested subgroups.
  • To address uncertainties in nuisance parameters for more reliable sample size calculations.
  • To propose an internal pilot study design for sample size recalculation.

Main Methods:

  • Derivation of multivariate exact and approximate distributions for standardized test statistics across subpopulations.
  • Development of sample size calculation procedures based on derived distributions.
  • Implementation of a blinded internal pilot study for reestimating variances and prevalences to adjust sample size.

Main Results:

  • The proposed methods do not significantly inflate Type I error rates.
  • Prespecified statistical power is maintained, provided the smallest subgroup sample size is adequate.
  • A lower boundary for internal pilot study sample size is established, particularly for small sample sizes.

Conclusions:

  • The developed methods offer a robust approach to sample size determination for complex subgroup analyses.
  • Internal pilot studies enhance the reliability of sample size calculations in the presence of parameter uncertainties.
  • Careful consideration of smallest subgroup sample size is vital for maintaining study power.