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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
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Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
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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...
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Predicting Gene Silencing Through the Spatiotemporal Control of siRNA Release from Photo-responsive Polymeric Nanocarriers
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Prediction intervals for overdispersed binomial data with application to historical controls.

Max Menssen1, Frank Schaarschmidt1

  • 1Abteilung Biostatistik, Institut für Zellbiologie und Biophysik, Leibniz Universität Hannover, Hannover, Germany.

Statistics in Medicine
|March 6, 2019
PubMed
Summary

Predicting future bioassay control outcomes is crucial. This study introduces calibrated prediction intervals accounting for overdispersion, improving accuracy for historical control data analysis.

Keywords:
alpha-calibration bootstrapbeta-binomialbioassayextra binomial variationquasi-binomial

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Area of Science:

  • Toxicology and Pharmacology
  • Biostatistics

Background:

  • Bioassays are essential for evaluating chemical compound effects on model organisms.
  • Standard practice involves comparing treatment groups to an untreated control group.
  • Historical control data accumulates with repeated bioassays, offering predictive potential.

Purpose of the Study:

  • To develop and evaluate statistical methods for predicting future control group outcomes in bioassays.
  • To address overdispersion in binomial count data common in bioassay historical controls.
  • To improve the reliability of prediction intervals for historical bioassay data.

Main Methods:

  • Two approaches for overdispersion were explored: quasi-binomial and beta-binomial distributions.
  • Bootstrap methods were used for alpha-calibration of prediction intervals.
  • A simulation study assessed coverage probabilities under various conditions (historical studies, sample sizes, overdispersion levels).

Main Results:

  • Alpha-calibration enhanced the coverage probabilities of both prediction interval types.
  • Calibrated intervals achieved satisfactory coverage close to 95% with at least 10 historical studies.
  • Methods were validated using real-world data from the NTP database for B6C3F1 mice.

Conclusions:

  • Calibrated prediction intervals effectively manage overdispersion in bioassay historical control data.
  • The proposed methods provide reliable predictions for future control group outcomes.
  • This approach enhances the statistical rigor of bioassay interpretation using historical data.