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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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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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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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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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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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An All-in-one Sample Holder for Macromolecular X-ray Crystallography with Minimal Background Scattering
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Incurred Sample Reanalysis: Time to Change the Sample Size Calculation?

Piotr J Rudzki1, Przemysław Biecek2, Michał Kaza3

  • 1Pharmacokinetics Department, Pharmaceutical Research Institute, 8 Rydygiera Street, 01-793, Warsaw, Poland. p.rudzki@ifarm.eu.

The AAPS Journal
|February 13, 2019
PubMed
Summary
This summary is machine-generated.

Ensuring high-quality bioanalytical methods is crucial for drug development. This study suggests using a fixed number of incurred sample reanalysis (ISR) samples, rather than a fixed rate, to improve statistical validity in pharmacokinetic and toxicokinetic studies.

Keywords:
bioanalysisbioanalytical method validationbridging datahypergeometric distributionincurred sample reanalysis (ISR)

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Area of Science:

  • Bioanalytical Chemistry
  • Pharmacokinetics
  • Drug Development

Background:

  • High-quality bioanalytical methods are essential for reliable pharmacokinetic and toxicokinetic (PK/TK) studies.
  • Incurred sample reanalysis (ISR) is a regulatory requirement for bioanalytical method validation.
  • Current sample size estimation for ISR is debated and may impact study outcomes.

Purpose of the Study:

  • To evaluate the impact of clinical study size on ISR test passing rates.
  • To compare fixed ISR sample rates versus a fixed number of ISR samples.
  • To propose a statistically rationalized approach for ISR sample size determination.

Main Methods:

  • Application of the hypergeometric distribution model.
  • Analysis of ISR passing rates under different sample size calculation strategies (fixed rate vs. fixed number).
  • Comparison of passing rates across various clinical study sizes.

Main Results:

  • The passing rate of ISR tests is dependent on clinical study size when using a fixed sample rate.
  • A fixed number of ISR samples results in a passing rate less dependent on clinical study size.
  • The hypergeometric distribution adequately models the assessment of original and repeated bioanalytical data.

Conclusions:

  • A fixed number of ISR samples (e.g., 30) is recommended for all studies regardless of clinical study size.
  • The hypergeometric distribution can optimize ISR sample size and bridge data across methods.
  • Current ISR recommendations should be reconsidered for a more statistically sound and risk-controlled approach.