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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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Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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Multiple Comparison Tests01:13

Multiple Comparison Tests

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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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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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Null and Alternative Hypotheses01:16

Null and Alternative Hypotheses

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The actual hypothesis testing begins by considering two hypotheses. They are termed  the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.
The null hypothesis, denoted by H0 is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As  a result if you cannot accept the null, it requires some action.
The alternative hypothesis, denoted by H1 or Ha, is a claim about the...
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Multiple hypotheses testing procedures in clinical trials and genomic studies.

Qing Pan1

  • 1Department of Statistics, The George Washington University , Washington, DC , USA.

Frontiers in Public Health
|December 19, 2013
PubMed
Summary

This review compares hypothesis testing methods for clinical trials and genomic studies. It highlights global tests for overall conclusions and stepwise procedures for marker-specific findings, aiding in selecting appropriate statistical approaches.

Keywords:
false discovery ratefamily wise error rateglobal testmultiple hypotheses testingresampling methodstepwise procedure

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Area of Science:

  • Biostatistics
  • Genomics
  • Clinical Trials

Background:

  • Clinical trials and genomic studies utilize distinct hypothesis testing procedures.
  • Global tests (SUM, Two-Step, ALRT, IUT, MAX) are common in clinical trials.
  • Stepwise procedures are prevalent in genomic studies for marker-specific conclusions.

Purpose of the Study:

  • To review and compare hypothesis testing procedures used in clinical trials and genomic studies.
  • To elucidate the strengths and weaknesses of various tests under different conditions (e.g., homogeneous vs. non-homogeneous effects, unequal sample sizes).
  • To illustrate the application of these methods using real-world data.

Main Methods:

  • Comparison of global tests: SUM, Two-Step, Approximate Likelihood Ratio Test (ALRT), Intersection-Union Test (IUT), MAX test.
  • Discussion of stepwise procedures for controlling Family-Wise Error Rate (FWER) and False Discovery Rate (FDR).
  • Application of the Westfall-Young resampling method for correlated test statistics.
  • Illustration using Genome-Wide Association Study (GWAS) data from a Type 1 diabetes clinical trial.

Main Results:

  • SUM and Two-Step tests are most powerful for homogeneous treatment effects.
  • ALRT and MAX tests offer robustness against non-homogeneous treatment effects.
  • ALRT demonstrates robustness to unequal sample sizes.
  • False Discovery Rate (FDR) is often preferred over FWER in high-dimensional genomic screening due to interpretability.
  • The Westfall-Young method preserves the correlation structure of P-values.

Conclusions:

  • The choice of hypothesis testing procedure depends on the study design and data characteristics.
  • Understanding the properties of different tests is crucial for accurate interpretation of clinical trial and genomic study results.
  • The presented methods and examples provide guidance for selecting appropriate statistical tools in biomedical research.