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Related Concept Videos

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

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...
Crossover Experiments01:16

Crossover Experiments

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.
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
Multiple Comparison Tests01:13

Multiple Comparison Tests

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...
Bioavailability Study Design: Single Versus Multiple Dose Studies01:11

Bioavailability Study Design: Single Versus Multiple Dose Studies

Bioavailability studies are essential for understanding how a drug is absorbed, distributed, metabolized, and excreted in the body. These studies assess the extent and rate at which the active pharmaceutical agent becomes available at the site of action. The design of bioavailability studies can involve single-dose or multiple-dose regimens, each with distinct advantages and limitations.Single-dose studies are the preferred approach due to their simplicity and reduced drug exposure for...
Clinical Trials01:16

Clinical Trials

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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A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
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Multiple comparisons in complex clinical trial designs.

H M James Hung1, Sue-Jane Wang

  • 1Division of Biometrics I, OB/OTS/CDER, US FDA, 10903 New Hampshire Ave, HFD-710, Silver Spring, MD 20993-0002, USA. hsienming.hung@fda.hhs.gov

Biometrical Journal. Biometrische Zeitschrift
|April 27, 2013
PubMed
Summary
This summary is machine-generated.

Standard multiple comparison procedures in clinical trials may be insufficient for complex designs. This study proposes expanded strategies to address challenges in evaluating statistical evidence across multiple trials and endpoints.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Regulatory Science

Background:

  • Multiple comparisons are critical for evaluating statistical evidence in clinical trials for regulatory submissions.
  • Current multiple testing paradigms may not adequately address the complexities of modern clinical trial designs, including multiple doses or combined endpoints.
  • Challenges arise in interpreting results from individual trials and multiple trials, especially with group sequential designs and specific endpoints like mortality.

Purpose of the Study:

  • To discuss the limitations of per-trial multiple comparison procedures in complex clinical trial scenarios.
  • To expand the utility of multiple comparison procedures for intricate trial designs and endpoint combinations.
  • To propose viable strategies for addressing statistical challenges in regulatory clinical trials.

Main Methods:

  • Review and critique of existing multiple comparison procedures in the context of advanced clinical trial methodologies.
  • Exploration of complexities introduced by multi-dose trials, combined mortality/morbidity endpoints, and symptomatic endpoints.
  • Development and stipulation of expanded strategies for multiple comparison procedures applicable to complex trial designs.

Main Results:

  • Identified insufficiency of traditional per-trial multiple comparison methods for complex clinical trial designs.
  • Highlighted challenges in applying standard procedures to multi-dose studies and trials with combined or sequential endpoints.
  • Proposed a framework for expanding multiple comparison procedures to accommodate intricate trial structures.

Conclusions:

  • Existing multiple comparison paradigms require adaptation to meet the demands of complex clinical trial designs.
  • Expanded strategies are necessary for robust statistical evidence evaluation in regulatory applications involving multiple trials and diverse endpoints.
  • The proposed approaches offer viable solutions for enhancing the interpretation of clinical trial results under complex conditions.