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Related Concept Videos

Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...

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Related Experiment Video

Updated: Jun 28, 2026

The Attentional Set Shifting Task: A Measure of Cognitive Flexibility in Mice
09:15

The Attentional Set Shifting Task: A Measure of Cognitive Flexibility in Mice

Published on: February 4, 2015

Gatekeeping testing via adaptive alpha allocation.

Jianjun' David ' Li1, Devan V Mehrotra

  • 1Wyeth Research Devision, 500 Arcola Road, Collegeville, PA 19425, USA.

Biometrical Journal. Biometrische Zeitschrift
|October 22, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new gatekeeping method for clinical trials to control statistical errors when analyzing multiple endpoints. The proposed procedure offers improved efficiency and avoids logical issues found in existing methods.

Related Experiment Videos

Last Updated: Jun 28, 2026

The Attentional Set Shifting Task: A Measure of Cognitive Flexibility in Mice
09:15

The Attentional Set Shifting Task: A Measure of Cognitive Flexibility in Mice

Published on: February 4, 2015

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methodology

Background:

  • Clinical trials frequently assess multiple primary and secondary endpoints.
  • Statistical significance in primary endpoints often prompts analysis of secondary endpoints for treatment effect characterization.
  • Regulatory bodies mandate control of familywise type I error rate when multiple endpoints are analyzed.

Purpose of the Study:

  • To propose a novel, practical, and logically sound gatekeeping procedure for clinical trials.
  • To address deficiencies in existing gatekeeping methods that may allow secondary endpoints to influence primary endpoint outcomes.
  • To demonstrate the efficiency of the new method compared to current approaches.

Main Methods:

  • Development of a new gatekeeping procedure designed to maintain logical consistency.
  • Application of the proposed method to a real-world clinical trial dataset.
  • Conducting simulation studies to compare the efficiency of the novel procedure against existing gatekeeping strategies.

Main Results:

  • The proposed gatekeeping method is easy to implement and logically coherent.
  • The new procedure demonstrates efficiency gains over existing gatekeeping methods.
  • Real data example and simulations confirm the advantages of the novel approach.

Conclusions:

  • The developed gatekeeping procedure offers a superior alternative for managing multiple endpoints in clinical trials.
  • This method enhances the statistical rigor and efficiency of clinical trial analysis.
  • The proposed procedure provides a reliable tool for regulatory compliance and robust treatment effect evaluation.