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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Dose Size and Dosing Frequency: Determination Methods01:21

Dose Size and Dosing Frequency: Determination Methods

Determining the optimal dose size and dosing frequency in pharmacotherapy is crucial for achieving therapeutic effectiveness while minimizing adverse effects. This article explores the methodologies employed in determining these parameters, focusing on their significance and interplay to tailor dosing regimens.Dose Size: Dose size refers to the amount of a drug administered in a single dose. It is determined based on the drug's pharmacodynamics and pharmacokinetics properties and...
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...

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Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
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Potential uncertainty reduction in model-averaged benchmark dose estimates informed by an additional dose study.

Kan Shao1, Mitchell J Small

  • 1Civil and Environmental Engineering, Porter Hall 119, Frew St., Pittsburgh, PA 15213, USA. kshao@cmu.edu

Risk Analysis : an Official Publication of the Society for Risk Analysis
|March 11, 2011
PubMed
Summary

This study presents a method to determine the value of adding dose levels in bioassay studies. Optimizing future experiments can reduce uncertainty in toxicity metrics like benchmark dose (BMD).

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Area of Science:

  • Toxicology
  • Biostatistics
  • Risk Assessment

Background:

  • Bioassay studies are crucial for toxicity assessment.
  • Uncertainty in toxicity metrics can impact risk assessment.
  • Optimizing experimental design can improve data reliability.

Purpose of the Study:

  • To develop a methodology for assessing the information value of additional dosage experiments in bioassay studies.
  • To provide insights for optimizing future study designs.
  • To quantify the potential reduction in uncertainty for toxicity metrics.

Main Methods:

  • Utilized Bayesian methods for dose-response model fitting.
  • Employed Markov chain Monte Carlo (MCMC) simulation for parameter estimation.
  • Applied Bayesian model averaging (BMA) for model comparison and combination.
  • Performed Monte Carlo analysis to identify optimal additional dosage points.

Main Results:

  • Demonstrated potential reduction in uncertainty of toxicity metrics.
  • Identified optimal additional dosage points for maximizing uncertainty reduction.
  • Quantified uncertainty reduction in terms of reduced interval widths and increased BMDL values.
  • Example showed ~30% reduction in uncertainty interval width and 5-10% increase in BMDL.

Conclusions:

  • The proposed methodology effectively assesses the value of additional experimental data.
  • Optimizing dose selection in future studies can significantly reduce uncertainty in toxicity predictions.
  • Informing dose-response model fitting and interpretation enhances study design.