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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
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Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...
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.
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...

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An R-Based Landscape Validation of a Competing Risk Model
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Published on: September 16, 2022

Evaluating variability and uncertainty separately in microbial quantitative risk assessment using two R packages.

Régis Pouillot1, Marie Laure Delignette-Muller

  • 17403 Wyndale lane, Chevy Chase, MD 20815, United States.

International Journal of Food Microbiology
|August 3, 2010
PubMed
Summary

New R packages, fitdistrplus and mc2d, enhance quantitative risk assessment for food safety. These tools aid in fitting data distributions and performing advanced Monte Carlo simulations, improving regulatory decision-making.

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Area of Science:

  • Food Safety
  • Risk Assessment
  • Computational Statistics

Background:

  • Quantitative risk assessment (QRA) is crucial for evidence-based regulatory decisions in food safety.
  • Existing computational tools may not adequately address the complexities of data censoring and uncertainty propagation in QRA.
  • There is a need for specialized software to streamline QRA processes and improve the scientific rigor of regulatory science.

Purpose of the Study:

  • Introduce two novel R packages, "fitdistrplus" and "mc2d", designed to support quantitative risk assessors.
  • Demonstrate the utility of these packages in handling censored data, fitting distributions, and conducting second-order Monte Carlo simulations.
  • Illustrate the application of these tools in a real-world food safety risk assessment scenario.

Main Methods:

  • Utilized the "fitdistrplus" R package for parametric univariate distribution fitting to continuous and discrete datasets, including censored data.
  • Employed bootstrap procedures within "fitdistrplus" to assess parameter uncertainty and incorporate it into risk models.
  • Applied the "mc2d" R package for second-order Monte Carlo simulations, enabling the separation and propagation of variability and uncertainty through probabilistic models.

Main Results:

  • "fitdistrplus" provides robust methods for selecting and fitting distributions to various data types, including censored data, with uncertainty quantification.
  • "mc2d" facilitates the construction and analysis of complex, multi-dimensional Monte Carlo simulations, effectively separating variability and uncertainty.
  • The combined application of these packages was successfully demonstrated in a case study assessing the risk of E. coli O157:H7 in ground beef.

Conclusions:

  • The "fitdistrplus" and "mc2d" R packages offer valuable computational resources for quantitative risk assessors in the food safety domain.
  • These packages improve the handling of complex data structures and uncertainty, leading to more robust and scientifically sound risk assessments.
  • The availability of these free, open-source tools promotes wider adoption and advancement in regulatory science and food safety decision-making.