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Related Concept Videos

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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Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

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Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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Controls in Experiments01:13

Controls in Experiments

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When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
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Randomized Experiments01:13

Randomized Experiments

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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...
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Crossover Experiments01:16

Crossover Experiments

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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
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Blinding01:11

Blinding

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Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
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Recommendations on multiple testing adjustment in multi-arm trials with a shared control group.

Dena R Howard1, Julia M Brown1, Susan Todd2

  • 11 Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK.

Statistical Methods in Medical Research
|September 21, 2016
PubMed
Summary
This summary is machine-generated.

Multi-arm trials with shared controls reduce false positive rates, making family-wise error rate (FWER) adjustments unnecessary solely for this reason. However, adjustments may be needed if multiple hypotheses increase the overall chance of false claims.

Keywords:
Multiple testingalpha adjustmentcorrelated comparisonsfamily-wise error ratemulti-arm clinical trialmultiplicityshared control grouptype-I error

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Pharmaceutical Research

Background:

  • Multi-arm clinical trials offer efficiency by testing multiple treatments against a single control group.
  • Debate exists regarding the necessity of multiple testing adjustments to control the family-wise type-I error rate (FWER).

Purpose of the Study:

  • To evaluate the impact of shared control data on FWER in multi-arm trials.
  • To determine the necessity of FWER adjustments in multi-arm clinical trial designs.

Main Methods:

  • Statistical analysis of correlations between treatment comparisons due to shared control data.
  • Demonstration of the effect of correlation on FWER in multi-arm trials.

Main Results:

  • Correlation between comparisons in multi-arm trials reduces the FWER, negating the need for adjustment solely due to shared controls.
  • The probability of multiple false positive outcomes increases, which standard FWER adjustments do not address.
  • A critical value adjustment is proposed for maintaining evidence standards in correlated comparisons.

Conclusions:

  • FWER adjustment is not required simply because control data is shared in multi-arm trials.
  • Adjustment is warranted only when testing multiple hypotheses increases the overall risk of false positive findings.
  • Shared controls in multi-arm trials can eliminate bias when comparing competing experimental therapies.