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Screening and Diagnostic Tests
Published in Marcello Pagano, Kimberlee Gauvreau, Heather Mattie, Principles of Biostatistics, 2022
Marcello Pagano, Kimberlee Gauvreau, Heather Mattie
Bayes' theorem is particularly important in diagnostic testing and screening. It relates the positive and negative predictive values of a test to its sensitivity and specificity, as well as the prevalence of disease in the population being tested.
Meta-Analysis of Diagnostic Tests
Published in Christopher H. Schmid, Theo Stijnen, Ian R. White, Handbook of Meta-Analysis, 2020
Yulun Liu, Xiaoye Ma, Yong Chen, Theo Stijnen, Haitao Chu
A pair of commonly used indices are sensitivity (Se) and specificity (Sp). Sensitivity is defined as the probability of testing positive given a person being diseased, and specificity is the probability of testing negative given a person being disease-free (Pepe, 2003). In practice, they can be used to address questions on how well the test reflects the true disease status. For example, suppose that, in one study, 240 participants are tested by both ultrasound and arthroscopy (treated as a gold standard) for rotator cuff tears, and test results are compared in a cross-tabulated 2 × 2 table (Table 19.1). In this study, Se is estimated as P(Ultrasound = +|Diseased) = 32/40 = 0.8 and Sp is estimated as P(Ultrasound = −|Non-diseased) = 180/200 = 0.9. Ideally, a gold standard test has a Se and Sp of 1. Other frequently used indices include positive and negative predictive values (PPV and NPV), and positive and negative diagnostic likelihood ratios (LR+ and LR−). PPV is the probability of diseased given a positive test result and NPV is the probability of disease-free given a negative test result. In this example, PPV is estimated as P(Diseased|Ultrasound = +) = 32/52 = 0.62 and NPV is estimated as P(Non-diseased|Ultrasound = −) = 180/188 = 0.96.
Acute and Recurrent Genital Herpes Simplex Virus Infection
Published in Marie Studahl, Paola Cinque, Tomas Bergström, Herpes Simplex Viruses, 2017
The value of screening all genitourinary medicine clinic attenders or antenatal patients for HSV antibodies has not been established. The possibility of false positive test results should be remembered. A test with a sensitivity of 97% and specificity of 96% has positive and negative predictive values of 97% and 96%, respectively, when used in a study population where the prevalence of HSV-2 is 50% (for example, a STD clinic patients presenting with genital ulcers). However, in a population with a HSV-2 prevalence of 5% (for example, the general population of some European countries), while the negative predictive value is almost 100%, the positive predictive value declines to 63%.
On diagnostic accuracy measure with cut-points criterion for ordinal disease classification based on concordance and discordance
Published in Journal of Applied Statistics, 2023
Jing Kersey, Hani Samawi, Jingjing Yin, Haresh Rochani, Xinyan Zhang
A diagnostic test result does not accurately represent the patient's condition because diagnostic tests rarely have perfect accuracy. Here accuracy refers to the probability of a correct test result. It is essential to develop quantitative methods to measure diagnostic accuracy. Some of the well-established before test measures (which relates to the inherent discriminatory accuracy of a test given the true disease conditions) are sensitivity (Se or TPR) and specificity (Sp or TNR), Youden index, the area under the ROC curve (AUC), diagnostic odds ratio (DOR). Additionally, after test measures tell us a person’s chance of having the disease given the test results and examples are likelihood ratios (LR) and positive and negative predictive values (PPV and NPV). These measurements can be used to decide whether to accept a diagnosis of disease, rule one out or order more testing [1].
COVID-19 diagnosis: lessons to learn and hints for preparedness
Published in Expert Review of Molecular Diagnostics, 2022
Saeed Samadizadeh, Britt Nakstad, Zahra Jamalpoor, Alireza Tahamtan
At present, it seems inadvisable to opt for other than a PCR test as the mainstream method for detecting viral genomes, as many other parallel techniques are not yet thoroughly assessed and qualified in practice. However, the RT-PCR test occasionally returns false-negative and false-positive results, contributing to the under or over-diagnosis of the disease [4,5]. Although sensitivity and specificity are substantial in prioritizing a test, the positive and negative predictive values (PPV and NPV, respectively) inform us about its accuracy and ultimate reliability in a clinical setting [6]. Moreover, disease prevalence is another significant but somewhat disregarded variable influencing PPV, NPV, and interpretation of the results. Accordingly, excessive false-negative reports are more likely to occur in high-prevalence populations, whereas low-prevalence settings may represent more false-positive outcomes [5]. In retrospect, we notice that the COVID-19 prevalence in different regions has been considerably variable and subjected to factors such as emerging variants and preventive measures. This diversity demands flexible diagnostic strategies to provide reliable test formats and reduce misdiagnosis. In low-prevalence communities, for example, to avoid the impact of high-rate false-positive results on the test validity, only symptomatic or high-risk individuals should be tested. Therefore, such statistical viewpoints are crucial to defining practical diagnostic guidelines based on specific criteria for different populations.
Revisiting approaches to and considerations for urinalysis and urine culture reflexive testing
Published in Critical Reviews in Clinical Laboratory Sciences, 2022
Allison B. Chambliss, Tam T. Van
Criteria for reflexing to urine culture vary among institutions, most likely due to the lack of standard guidelines on UA parameters that are predictive of urine culture results. The most common UA parameters used are WBC counts, presence of LE, reduction of nitrate to nitrite, and the presence of bacteria on microscopy (Table 2). The sensitivity and specificity and/or positive and negative predictive values (PPV and NPV) of these factors alone or in combination vary. For pediatric patients 2–24 months of age, the presence of nitrite demonstrated poor sensitivity (53%) and high specificity (98%), suggesting that false-positive results are rare but negative results cannot be used to rule out infection, particularly if the UTI is caused by an organism that does not metabolize nitrate [26]. While LE may have better sensitivity (83%) than nitrite in this age group, the lower specificity of 78% necessitates caution in interpreting a positive LE result [26]. Similarly, WBC counts and bacteria on microscopy showed acceptable but not definitive sensitivity and specificity in predicting UTIs. To minimize the likelihood of missing a potential true case of infection, many institutions use a combination of UA parameters, such as the presence of pyuria (WBC) or positive nitrite and/or LE, as screening for UTIs. Setting the criteria for a UA reflexive testing algorithm becomes a balancing exercise for the best PPV for positive culture while minimizing the number of potentially missed cases [50–52].