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Statistical considerations of the random selection process in a drug-test program
C A Burtis1, J H Owings, R S Leete
1Chemical Technology Division, Oak Ridge National Laboratory, TN.
Clinical Chemistry
|October 1, 1987
Summary
Random selection in drug testing ensures fairness and deters drug use by creating uncertainty. Mathematical models, like the Poisson distribution, can optimize testing frequency for specific program needs.
Area of Science:
- Occupational Health
- Applied Mathematics
- Forensic Science
Background:
- Prospective drug-testing programs select individuals from a pool of sensitive job classifications.
- Random selection provides a fair and impartial method for choosing individuals for testing.
- The uncertainty of selection enhances the deterrent effect of drug testing programs.
Purpose of the Study:
- To explore the application of random selection in drug testing.
- To demonstrate how mathematical probability distributions can model and optimize drug testing programs.
- To analyze the use of Poisson distribution in predicting selection frequency and structuring testing protocols.
Main Methods:
- Utilizing a prospective drug-testing program framework.
- Implementing random selection from a defined pool of employees.
- Applying Poisson distribution to model selection probabilities over time.
- Analyzing the impact of sampling frequency on selection probabilities.
Main Results:
- Random selection ensures each individual has an equal probability of being tested.
- The Poisson distribution accurately predicts the number of selections within a given timeframe.
- Testing frequency directly influences the probability of an individual being selected.
- Mathematical modeling allows for the optimization of drug testing program parameters.
Conclusions:
- Random selection is a fair and effective method for drug testing.
- Poisson distribution provides a robust mathematical tool for managing and optimizing drug testing programs.
- The frequency of drug testing can be adjusted to meet specific program objectives, such as minimizing or maximizing selection probabilities.
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