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Related Concept Videos

Percentile01:18

Percentile

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A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile.
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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Estimation of k and VD of Aminoglycosides01:20

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Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
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Estimation of the Physical Quantities01:05

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Estimating Population Mean with Known Standard Deviation01:16

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Methods and applications of percentile estimation.

Qi Xia1,2, Yi Tsong3, Yu-Ting Weng3

  • 1The Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, PA, USA.

Journal of Biopharmaceutical Statistics
|June 26, 2019
PubMed
Summary

A new UMOVER method offers an efficient alternative for percentile estimation in statistical applications. This approach provides easy implementation and comparable performance to exact t methods for cut point determination.

Keywords:
MOVERScreening assay cut pointbioequivalenceconfirmatory assay cut pointimmunogenicity testingpercentile estimation

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Area of Science:

  • Statistics
  • Biostatistics
  • Pharmacometrics

Background:

  • Percentiles are crucial in statistical applications, particularly in assay development for immunogenicity testing.
  • FDA guidelines recommend using the lower confidence limit of the negative subject population percentile as a cut point for false-positive rate control.
  • Existing exact t-based methods for percentile estimation can be computationally intensive and complex.

Purpose of the Study:

  • To propose the UMOVER method as a computationally efficient alternative for percentile estimation.
  • To evaluate the UMOVER method's performance for screening and confirmatory cut point determination in immunogenicity testing.
  • To extend the UMOVER method for comparing percentiles between test and reference products.

Main Methods:

  • The study introduces and applies the UMOVER (Uniformly Most Optimal Variance) method for percentile estimation.
  • Performance comparison of the UMOVER method against existing exact t and approximated approaches.
  • Numerical studies were conducted to validate the extended UMOVER method for comparative analyses.

Main Results:

  • The UMOVER method demonstrates easy implementation and comparable performance to the exact t approach.
  • The proposed method is suitable for determining screening and confirmatory cut points in immunogenicity assays.
  • The extended UMOVER method effectively facilitates the comparison of percentiles between test and reference products.

Conclusions:

  • The UMOVER method presents a practical and efficient alternative to traditional percentile estimation techniques.
  • This method can aid in robust cut point determination, ensuring reliable immunogenicity testing.
  • The UMOVER method's applicability extends to comparative analyses, supporting product development and evaluation.