Requirements for Minimum Sample Size for Sensitivity and Specificity Analysis (original) (raw)
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Diagnostics
Sample size calculation based on a specified width of 95% confidence interval will offer researchers the freedom to set the level of accuracy of the statistics that they aim to achieve for a particular study. This paper provides a description of the general conceptual context for performing sensitivity and specificity analysis. Subsequently, sample size tables for sensitivity and specificity analysis based on a specified 95% confidence interval width is then provided. Such recommendations for sample size planning are provided based on two different scenarios: one for a diagnostic purpose and another for a screening purpose. Further discussion on all the other relevant considerations for the determination of a minimum sample size requirement and on how to draft the sample size statement for performing sensitivity and specificity analysis are also provided.
A simple nomogram for sample size for estimating sensitivity and specificity of medical tests
Indian journal of ophthalmology
Sensitivity and specificity measure inherent validity of a diagnostic test against a gold standard. Researchers develop new diagnostic methods to reduce the cost, risk, invasiveness, and time. Adequate sample size is a must to precisely estimate the validity of a diagnostic test. In practice, researchers generally decide about the sample size arbitrarily either at their convenience, or from the previous literature. We have devised a simple nomogram that yields statistically valid sample size for anticipated sensitivity or anticipated specificity. MS Excel version 2007 was used to derive the values required to plot the nomogram using varying absolute precision, known prevalence of disease, and 95% confidence level using the formula already available in the literature. The nomogram plot was obtained by suitably arranging the lines and distances to conform to this formula. This nomogram could be easily used to determine the sample size for estimating the sensitivity or specificity of a diagnostic test with required precision and 95% confidence level. Sample size at 90% and 99% confidence level, respectively, can also be obtained by just multiplying 0.70 and 1.75 with the number obtained for the 95% confidence level. A nomogram instantly provides the required number of subjects by just moving the ruler and can be repeatedly used without redoing the calculations. This can also be applied for reverse calculations. This nomogram is not applicable for testing of the hypothesis set-up and is applicable only when both diagnostic test and gold standard results have a dichotomous category.
Mathematics
Sample size calculation in biomedical practice is typically based on the problematic Wald method for a binomial proportion, with potentially dangerous consequences. This work highlights the need of incorporating the concept of conditional probability in sample size determination to avoid reduced sample sizes that lead to inadequate confidence intervals. Therefore, new definitions are proposed for coverage probability and expected length of confidence intervals for conditional probabilities, like sensitivity and specificity. The new definitions were used to assess seven confidence interval estimation methods. In order to determine the sample size, two procedures—an optimal one, based on the new definitions, and an approximation—were developed for each estimation method. Our findings confirm the similarity of the approximated sample sizes to the optimal ones. R code is provided to disseminate these methodological advances and translate them into biomedical practice.
2014
results obtained from diagnostic screening tests. These indices include sensitivity, specificity, prevalence rates and false rates. We here present statistical methods for estimating these rates and for testing hypotheses concerning them. An estimate of the proportion of a population expected to test positive in a diagnostic screening test is also provided. Further interest is also to estimate the sensitivity and specificity of the test and then the false rates as functions of sensitivity and specificity given knowledge or availability of an estimate of the prevalence rate of a condition in a population. The indices proposed ranges from -1 to 1 inclusively and therefore enables the researcher to determine if an association exists and if it exists between test results and condition as well as whether it is positive and direct or negative and indirect which will serve as an advantage over the traditional methods. The proposed indices provide estimates of the test statistic. When the proposed measures are applied, results indicate that it is easier to interpret and understand more than those obtained using the traditional approaches. In addition, the proposed measure is shown to be at least as efficient and hence as powerful as the traditional methods when applied to sample data
Variation of a test's sensitivity and specificity with disease prevalence
Canadian Medical Association Journal, 2013
Research CMAJ Background: Anecdotal evidence suggests that the sensitivity and specificity of a diagnostic test may vary with disease prevalence. Our objective was to investigate the associations between disease prevalence and test sensitivity and specificity using studies of diagnostic accuracy.
Controlled Clinical Trials, 2004
During the design stage of a study to assess the population sensitivity (P S) (or specificity) of a diagnostic test, the number of subjects (N) who will be administered both a gold standard test and a new test needs to be calculated. A common approach is to calculate the number of cases (n) with a specific disease or condition as diagnosed by the gold standard test first, and then to determine N based on the prevalence or incidence rate of the disease (P P) in the population, calculated as N = n/P P. Due to sampling variation, given the sample size N, the number of cases having the disease identified by the gold standard test could be less than N Â P P. In this case, the study would be under-powered and may fail to produce an unbiased and precise estimate. In this study, we investigated this possibility for a situation where the required sample size is calculated using the confidence interval approach. When the sampling variation is considered, the variance of the sample sensitivity is slightly inflated, but its confidence interval width becomes widely dispersed. In order to reach the originally designed precision, adjustment in the sample size, N, is needed and suggested in this paper.
Sensitivity and Specificity Dependent Measure of Association in Diagnostic Screening Tests
2013
This paper proposes and presents a measure of the strength of association between test results and state of nature or condition in a population constructed using only sensitivity and specificity of diagnostic screening tests. The proposed measure which always has between -1 and 1 inclusively enables the researcher to determine not only if an association exists between test results and condition, but if such an association exists, whether it is positive and direct or negative and indirect thereby giving the measure an advantage over the traditional odds ratio method. Estimates of the standard error and test statistic for the proposed measure are provided. Results using the proposed measure are shown to be easier to interpret and understand than those obtained using the traditional odds ratio approach. Furthermore, using sample data, the proposed measure is shown to be at least as efficient and hence as powerful as the traditional odds ratio.
Final Sensitivity and Specificity of Different Test Combinations in Screening
JMED Research, 2014
Screening is a way of looking for patients who do not seem to have major medical problems. Screening tests usually include several phases. In double phase screening, one test is to find the potential patient, and then another test is to prove the disease in those patients. The combination of these two tests is important and can be either "in series", where running or not running the second test depends on the results of the first one, or "in parallel" where the second test is independently performed regardless of what the first test result is. In this article we compared the combined tests based on their final sensitivity and specificity using mathematical analysis to determine how the order of different tests with different sensitivity, and specificity can impact the final specificity and sensitivity of the combined tests. We could prove that in none of the combined tests the final sensitivity and specificity is affected by the sequence of the tests, but the amount of intermediate false positive is affected, so that if the first test is more sensitive, we will have more false positives after the first test. The type of screening we choose relies on what our aim is for doing that screening, and a combination of tests should be selected, so that the selected tests are acceptable in terms of false positive but whether, to do the more sensitive test first or the more specific one, there is no difference in the final sensitivity or specificity.
Journal of Applied Statistics, 2014
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