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Related Concept Videos

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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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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FPCA-based method to select optimal sampling schedules that capture between-subject variability in longitudinal

Meihua Wu1, Ana Diez-Roux2, Trivellore E Raghunathan3

  • 1Gilead Sciences, Inc., Foster City, California 94404, U.S.A.

Biometrics
|May 9, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for optimizing longitudinal study sampling schedules using functional principal component analysis (FPCA). The approach effectively captures between-individual variability, outperforming traditional methods in complex scenarios.

Keywords:
Longitudinal designNonlinear model designOptimal designTemporal pattern

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Study Design Optimization

Background:

  • Determining optimal sampling schedules is crucial for longitudinal studies.
  • Accurate estimation of mean profiles and between-subject variability is essential.
  • Existing methods have limitations in capturing between-individual variability.

Purpose of the Study:

  • To propose a novel approach for deriving optimal sampling schedules.
  • To enhance the capture of between-individual variability in longitudinal data.
  • To improve upon existing study design methodologies.

Main Methods:

  • Utilized functional principal component analysis (FPCA) to characterize mean and variability.
  • Developed a new approach for optimizing sampling schedules based on FPCA.
  • Conducted simulation studies comparing the new approach with parametric mixed models (PMM).

Main Results:

  • The FPCA-based approach effectively characterizes both mean and variability.
  • The proposed method performs comparably to PMM when PMM is adequate.
  • The FPCA approach outperforms PMM-based methods when data deviates from PMM assumptions.

Conclusions:

  • The FPCA-based sampling schedule design is a robust and flexible approach.
  • This method enhances the characterization of longitudinal data variability.
  • Applied to design studies for salivary cortisol and urinary progesterone profiles.