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Related Concept Videos

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
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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.
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Accuracy and Errors in Hypothesis Testing01:13

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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.
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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.
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Related Experiment Video

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Task Interruption and Resumption Paradigm for Testing the Activation and Pursuit of an Abstract Thinking Goal
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Hypothesis testing for two-stage designs with over or under enrollment.

Donglin Zeng1, Fei Gao1, Kuolung Hu2

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, U.S.A.

Statistics in Medicine
|March 27, 2015
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Summary

This study addresses issues with Simon's two-stage designs in cancer trials when second-stage sample sizes change. It proposes a method to maximize power and maintain accuracy for treatment efficacy assessment.

Keywords:
adaptive designclinical trialssample size modification

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Oncology

Background:

  • Simon's two-stage designs are standard for phase II cancer trials.
  • Deviations in the second-stage sample size invalidate standard inference procedures.
  • Existing computational methods for modified designs have limitations.

Purpose of the Study:

  • To develop a valid statistical procedure for Simon's two-stage designs with modified second-stage sample sizes.
  • To maximize unconditional power while controlling type I error rates.
  • To provide accurate confidence intervals for treatment response rates.

Main Methods:

  • Utilized a normal approximation for accurate power computation, even with small sample sizes.
  • Developed a method to adjust inference procedures for modified second-stage sample sizes.
  • Constructed confidence intervals by inverting the hypothesis test.

Main Results:

  • The normal approximation for power calculation demonstrated high accuracy.
  • The proposed method effectively controls type I error.
  • Confidence intervals exhibited reasonable coverage probabilities.

Conclusions:

  • The developed statistical approach is valid for phase II cancer trials with modified second-stage sample sizes.
  • This method offers accurate power calculations and reliable confidence intervals.
  • It provides a practical solution for real-world clinical trial scenarios.