Incorporating the sampling variation of the disease prevalence when calculating the sample size in a study to determine the diagnostic accuracy of a test (original) (raw)
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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.
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.
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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.
Alternative Confidence Interval Methods Used in the Diagnostic Accuracy Studies
Computational and mathematical methods in medicine, 2016
Background/Aim. It is necessary to decide whether the newly improved methods are better than the standard or reference test or not. To decide whether the new diagnostics test is better than the gold standard test/imperfect standard test, the differences of estimated sensitivity/specificity are calculated with the help of information obtained from samples. However, to generalize this value to the population, it should be given with the confidence intervals. The aim of this study is to evaluate the confidence interval methods developed for the differences between the two dependent sensitivity/specificity values on a clinical application. Materials and Methods. In this study, confidence interval methods like Asymptotic Intervals, Conditional Intervals, Unconditional Interval, Score Intervals, and Nonparametric Methods Based on Relative Effects Intervals are used. Besides, as clinical application, data used in diagnostics study by Dickel et al. (2010) has been taken as a sample. Results...
Archives of Microbiology and Immunology, 2023
In the process of medical diagnostics many types of tests are used, among them in vitro diagnostic laboratory tests. The performances of such tests are usually examined in clinical studies with a disease prevalence that is different from the prevalence of the disease in another clinical setting. The question then is whether diagnostic test characteristics like sensitivity, specificity and likelihood ratios are independent on the prevalence of the disease. The answer to this question is quite important when applying the test performance characteristics of a clinical study in a different clinical diagnostic setting. Here, it is demonstrated that, apart from special cases, test characteristics are indeed dependent on the prevalence of the disease. First, the underlying theoretical model of this dependence is demonstrated and, second, the model is validated with three practical diagnostic examples, i.e myocardial infarction, autoimmune disease, and vitamin-B12 deficiency.
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.
Sources of bias and variation in diagnostic accuracy studies [electronic resource] /
Variety in research designs can be advantageous. Knowledge of the performance of a test in different patient groups, for example, can help clinicians in their decision to order the test. In Chapter 2 Case-control and twogate designs in diagnostic accuracy studies, we discuss design aspects that influence the composition of the study group and explain how differences in study sampling may affect estimates of diagnostic accuracy.
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