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Related Concept Videos

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.
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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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Cochran's Q Test01:17

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Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
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Influence diagnostics for count data under AB-BA crossover trials.

Chengcheng Hao1, Dietrich von Rosen2,3, Tatjana von Rosen1

  • 11 Department of Statistics, Stockholm University, Stockholm, Sweden.

Statistical Methods in Medical Research
|November 25, 2015
PubMed
Summary

This study introduces new methods to measure how data changes affect results in AB-BA crossover trials with Poisson data. These diagnostic tools help assess the reliability of estimates derived from generalized linear mixed models.

Keywords:
Generalised mixed linear modelInfluential observationPoisson modelmodel diagnosticsperturbation scheme

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • AB-BA crossover studies are common in clinical trials for comparing treatments.
  • Data perturbations can potentially bias estimates in these studies.
  • Assessing the impact of data quality on study outcomes is crucial.

Purpose of the Study:

  • To develop novel diagnostic measures for evaluating data perturbation effects.
  • To specifically address influence on estimates in AB-BA crossover designs.
  • To analyze studies with Poisson distributed response variables.

Main Methods:

  • Utilized generalized linear mixed models (GLMMs) with normally distributed random effects.
  • Decomposed the model into independent sub-models for analytical tractability.
  • Derived closed-form expressions to quantify changes in maximum likelihood estimates.

Main Results:

  • Successfully developed and derived new influence measures.
  • Demonstrated the ability to evaluate estimate changes under various perturbation schemes.
  • Validated the performance of the proposed measures through simulations and real-world data.

Conclusions:

  • The developed diagnostic measures are effective in assessing data perturbation influence.
  • These methods enhance the robustness and reliability of AB-BA crossover study findings.
  • Provides a valuable tool for biostatisticians and clinical trial researchers.