Standard error and sample size determination for estimation of probabilities based on a test variable

A method of sample size determination for estimation of probabilities based on a test variable is presented. Applications to estimation of sensitivity and specificity of medical tests are the focus of this research, although the methods can be applied to other areas of study such as engineering reli...

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Bibliographic Details
Published in:Journal of clinical epidemiology 1996-04, Vol.49 (4), p.419-429
Main Authors: White, Donald B., James, Lancelot
Format: Article
Language:English
Subjects:
binomial
Biological and medical sciences
Epidemiologic Methods
Epidemiology
Estimation
General aspects
Humans
Lupus Erythematosus, Cutaneous - classification
Medical sciences
Methodology
Models, Statistical
normal
Poisson
Poisson Distribution
Probability
Public health. Hygiene
Public health. Hygiene-occupational medicine
Sample Size
Sampling Studies
sensitivity
Sensitivity and Specificity
specificity
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Summary:A method of sample size determination for estimation of probabilities based on a test variable is presented. Applications to estimation of sensitivity and specificity of medical tests are the focus of this research, although the methods can be applied to other areas of study such as engineering reliability. Examples are given for determining sample sizes required for the classification of patients with cutaneous lupus erythematosus based on the incidence of several markers. In this example, the test variable is the number of markers present. The methodology employs a weighted average of model-based and non-model-based estimates of the probability with the weights determined by the closeness to or the confidence in the given model. Formulas and charts required for determining sample size are provided for test variables that can be modeled by the binomial, Poisson, or normal distributions, i.e., for the most commonly encountered distributions for counting events (binomial and Poisson) and for measurements (normal). However, the methods given can be applied to any distribution, including multivariate. Especially when relatively small probabilities (the rare events) are being estimated, the techniques provided assistance in safeguarding against undersampling brought on by unwarranted confidence in a test variable distribution and against oversampling required for high accuracy in non-model-based probability estimators.
ISSN:0895-4356
1878-5921
DOI:10.1016/0895-4356(95)00570-6