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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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Sample Size Calculation01:19

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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One-Way ANOVA: Unequal Sample Sizes01:15

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Body: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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One-Way ANOVA: Equal Sample Sizes01:15

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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Censoring Survival Data01:09

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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...
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Related Experiment Video

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Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
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Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR

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Sample size re-estimation in crossover trials: application to the AIM HY-INFORM study.

Julie Wych1, Michael J Grayling2,3, Adrian P Mander2,4

  • 1Medical Research Council Biostatistics Unit, University of Cambridge, School of Clinical Medicine, Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK. julie.wych@mrc-bsu.cam.ac.uk.

Trials
|December 4, 2019
PubMed
Summary

Sample size re-estimation in crossover trials is crucial for long-term studies. This study provides a formula and simulation for interim sample size adjustments, ensuring robust trial power even with varying within-person standard deviations.

Keywords:
AIM HY-INFORM studyCrossover trialHypertensionSample size calculationSimulation study

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Hypertension Research

Background:

  • Crossover designs are common in chronic illness trials but require within-person standard deviation (SD) estimates for sample size calculations, which are often unavailable at study inception.
  • Interim sample size re-estimation designs offer a statistically robust method to adapt trial sample sizes mid-course using accrued data.
  • The AIM HY-INFORM study utilizes two crossover trials to investigate ethnicity's impact on blood pressure response to antihypertensive treatments.

Purpose of the Study:

  • To develop a formula for sample size re-estimation applicable to crossover trials.
  • To conduct a simulation study of the planned interim analysis to evaluate the impact of alternative within-person SDs on sample size requirements.
  • To assess the feasibility of maintaining adequate statistical power in the AIM HY-INFORM study under varying within-person SD assumptions.

Main Methods:

  • A formula for sample size re-estimation in crossover trials was derived.
  • A simulation study was performed to assess the planned interim analysis of the AIM HY-INFORM study.
  • The simulations investigated the impact of different within-person SD values on the required sample size for achieving desired statistical power.

Main Results:

  • The protocol-defined within-person SD of 8 mmHg allows for >90% power with 600 participants for a 4 mmHg treatment effect.
  • Simulations indicated that an increased within-person SD of 9 mmHg would necessitate 640 participants in a three-period, three-treatment design and 602 in a four-period, four-treatment design to maintain 90% power.
  • Even with a small increase in within-person SD, achieving 80% power is feasible without increasing the initial sample size of 600 participants.

Conclusions:

  • The presented formulas offer a validated method for re-estimating sample sizes in crossover trials.
  • Simulating the interim analysis for the AIM HY-INFORM study demonstrates the impact of potential increases in within-person SD.
  • The study suggests that maintaining adequate statistical power (80%) is achievable with the planned sample size of 600, even with a modest rise in the within-person SD.