Impact Of False Positives Research Articles

Utility of Stool-Based Tests for Colorectal Cancer Detection: A Comprehensive Review.

Colorectal cancer (CRC) is a significant global health issue where early detection is crucial for improving treatment outcomes and survival rates. This comprehensive review assesses the utility of stool-based tests in CRC screening, including traditional fecal occult blood tests (FOBT), both chemical (gFOBT) and immunochemical techniques (FIT), as well as multitarget stool DNA (mt-sDNA) as a novel and promising biomarker. The advancements, limitations and the impact of false positives and negatives of these methods are examined. The review analyzed various studies on current screening methods, focusing on laboratory tests and biomarkers. Findings indicate that while FIT and mt-sDNA tests offer enhanced sensitivity and specificity over traditional guaiac-based FOBT, they also come with higher costs and potential for increased false positives. FIT shows better patient adherence due to its ease to use, but incorrect usage and interpretation of FOBT can lead to significant diagnostic errors. In conclusion, despite the improvements in FOBT methods like FIT in CRC detection, careful consideration of each method's benefits and drawbacks is essential. Effective CRC screening programs should combine various methods tailored to specific population needs, aiming for early detection and reduced mortality rates.

Projected population mortality reduction from liquid biopsy screening approaches: High sensitivity screening for focused cancer types and populations—High specificity screening for broad cancer types and populations.

33 Background: There are two distinct ‘liquid biopsy’ cancer screening approaches. Focused testing for selected cancers where screening is recommended but not being received, in which test sensitivity can be prioritized as false positives reflex to standard of care screening. Broad testing for many cancers where false-positives necessitate high test specificity, typically at the expense of sensitivity. The public health impact and testing burden of these approaches has not been explored. Methods: Projected cancer deaths averted and annual number of tests performed based on published performance and target populations for liquid biopsy tests with published data. Focused screening modeled for patients who are not receiving guideline-recommended screening for lung, liver and colorectal cancer. Broad screening modeled for all individuals aged 50-80 and assumed to detect all cancer types. Averted deaths determined by combining the mortality reduction associated with screening (published literature, 20% if no literature); adjusted for reported prospective test sensitivities: focused (85%), broad (28%). Only individuals not receiving a particular cancer screening could have liquid biopsy avert a death for that cancer. Results: Focused and broad screening strategies each prevented approximately 20,000 cancer deaths annually. Focused screening required testing one-fifth as many people annually as the broad testing approach (30MM vs.150MM). All of the projected reduction in mortality associated with focused testing was based on randomized studies; for the broad strategy the equivalent statistic was 53%. Conclusions: Cancer deaths can be reduced equivalently through focused cancer screening at high sensitivity and broad cancer screening at high specificity at one-fifth the testing burden. The impact of false positives in the focused testing approach and false reassurance in the broad testing approach might alter these findings, which could also be affected by the cost of testing and follow-up. [Table: see text]

Fully Automated Annotation of Seismocardiogram for Noninvasive Vital Sign Measurements

This paper presents a fully automated procedure for acquiring and analyzing seismocardiographic (SCG) traces from an inertial measurement unit (IMU) placed over a subject's sternum. An automated calibration procedure allows for straightforward adaption to different subjects. Calibration is performed once per subject, exploiting electrocardiogram (ECG) markers; relevant patterns and parameters are automatically extracted and used for successive SCG processing, which does not require concurrent ECG information any longer. Annotation of SCG traces is performed in two steps: in the first one, a suitably engineered signal is derived from SCG and used as coarse heartbeat detector; then, the annotation can be performed by comparing the prototype extracted at calibration time with segments of SCG data, near to the detected beats. The proposed methodology is validated by direct comparison with ECG, adopted as gold standard. In particular, three main metrics are taken into account: sensitivity (i.e., the percentage of correctly identified heartbeats, compared to ECG), precision (i.e., impact of false positives on truly detected beats), and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (i.e., linearity between beat-to-beat measurements as computed by ECG and SCG). Results show satisfactory performance, more than adequate to continuous, long-term monitoring: overall, approximately 90% of heartbeats are correctly detected, on average, with minimal false positives (≈ 1%). Linearity between ECG- and SCG-computed beat-to-beat intervals is extremely high (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.95, on average), indicating good agreement between the two measurement methods. These results suggest that SCG can be used as a reliable, contactless measure of heart-related parameters.

Predicting Essential Proteins Based on Second-Order Neighborhood Information and Information Entropy

Essential proteins are critical components of living organisms and indispensable to cellular life. Identification of essential proteins plays a critical role in the survival and development of life process and understanding the function of cell machinery. The experimental methods are usually costly and time-consuming. In order to overcome these limitations, many computational methods have been proposed to discover essential proteins based on the topological features of PPI networks and other biological information. In this paper, a new method named NIE is proposed to predict essential proteins based on second-order neighborhood information and information entropy of protein complex and subcellular localization. Firstly, a number of studies have shown that the RNA-Seq data is more advantageous than traditional gene expression data in predicting essential proteins. Meanwhile, the protein essentiality is closely related to the subcellular localization information, protein complex information and protein GO terms through data analysis. A weighted PPI network is constructed to reduce the impact of false positives and false negatives data on the identification of essential proteins, which integrates the GO terms information with Pearson correlation coefficient of RNA-Seq data. Secondly, the information entropy of protein complexes and subcellular localization is calculated to represent the biological characteristics of proteins. Furthermore, an information propagation model is constructed, which combines the biological properties of the proteins with the second-order neighborhood information in the PPI network to measure the essentiality of the proteins. In the experiments section, the proposed method is implemented on three common datasets (DIP, Krogan and MIPS) of Saccharomyces cerevisiae. A comparison study with other commonly used algorithms, including LAC, NC, PeC, WDC, UC, LIDC and LBCC is performed to evaluate the performance of NIE. The results show that the new method NIE has a better performance in predicting essential proteins.

Fact or fiction: reducing the proportion and impact of false positives.

False positive findings in science are inevitable, but are they particularly common in psychology and psychiatry? The evidence that we review suggests that while not restricted to our field, the problem is acute. We describe the concept of researcher 'degrees-of-freedom' to explain how many false-positive findings arise, and how the various strategies of registration, pre-specification, and reporting standards that are being adopted both reduce and make these visible. We review possible benefits and harms of proposed statistical solutions, from tougher requirements for significance, to Bayesian and machine learning approaches to analysis. Finally we consider the organisation and methods for replication and systematic review in psychology and psychiatry.

Evidence-based Criteria for Assessment of Visual Field Reliability

Assess the impact of false-positives (FP), false-negatives (FN), fixation losses (FL), and test duration (TD) on visual field (VF) reliability at different stages of glaucoma severity. Retrospective. A total of 10 262 VFs from 1538 eyes of 909 subjects with suspect or manifest glaucoma and ≥5 VF examinations. Predicted mean deviation (MD) was calculated with multilevel modeling of longitudinal data. Differences between predicted and observed MD (ΔMD) were calculated as a reliability measure. The impact of FP, FN, FL, and TD on ΔMD was assessed using multilevel modeling. ΔMD associated with a 10% increment in FP, FN, and FL, or a 1-minute increase in TD. FL had little impact on ΔMD (<0.2 decibels [dB] per 10% abnormal catch trials), and no level of FL produced ≥1 dB of ΔMD at any disease stage. FP yielded greater than expected MD, with a 10% increment in abnormal catch trials associated with a ΔMD= 0.42, 0.73, and 0.66 dB in mild (MD >-6 dB), moderate (-6 ≤MD <-12 dB), and severe (-12 ≤MD ≤-20 dB) disease, respectively, up to 20% abnormal catch trials, and a ΔMD= 1.57, 2.06, and 3.53 dB beyond 20% abnormal catch trials. FNs generally produced observed MDs below expected MDs. FN were minimally impactful up to 20% abnormal catch trials (ΔMD per 10% increment >-0.14 dB at all levels of severity). Beyond 20% abnormal catch trials, each 10% increment in abnormal catch trials was associated with a ΔMD=-1.27,-0.53, and-0.51 dB in mild, moderate, and severe disease, respectively. |ΔMD| ≥1 dB occurred with 22% FP and 26% FN in early, 14% FP and 34% FN in moderate, and 16% FP and 51% FN in severe disease. A 1-minute increment in TD produced ΔMDs between-0.35 and-0.40 dB. FL have little impact on reliability in patients with established glaucoma. FP, and to a lesser extent FNs and TD, significantly affect reliability. The impact of FP and FN varies with disease severity and over the range of abnormal catch trials. On the basis of our findings, we present evidence-based, severity-specific standards for classifying VF reliability for clinical or research applications.

A Demonstration of Regression False Positive Selection in Data Mining

ABSTRACTBusiness analytics courses, such as marketing research, data mining, forecasting, and advanced financial modeling, have substantial predictive modeling components. The predictive modeling in these courses requires students to estimate and test many linear regressions. As a result, false positive variable selection (type I errors) is nearly certain to occur. This article describes an in‐class demonstration that shows the frequency and impact of false positives on data mining regression‐based predictive modeling. In this demonstration, 500 randomly generated independent (X) variables are individually regressed against a single, randomly generated (Y) variable, and the resulting 500 p‐values are sorted and examined. This experiment is repeated and the distribution of the number of variables significant at the 5% level resulting from this simulation is presented and discussed. The demonstration provides a tangible example in which students see the reality and risks of incorrectly inferring statistical significance of independent regression variables. Students have expressed a deeper understanding and appreciation of the risks of type I errors through this demonstration. This demonstration is innovative because the scale of the simulation allows the students to experience the near certainty that the correlations shown in the results are truly random.

UPDATE ON LINEAR NON-THRESHOLD DOSE-RESPONSE MODEL AND IMPLICATIONS FOR DIAGNOSTIC RADIOLOGY PROCEDURES

Cancer risk estimates are used in the setting of radiation protection standards by international and national organizations, and for this purpose need to be developed for low doses of radiation. The approach has involved extrapolation from cancer mortality and incidence values at higher doses to predict the low-dose estimates. Such an extrapolation has generally involved the use of the linear non-threshold (LNT) theory. Recent reports from the National Research Council (BEIR VII) and the International Commission on Radiological Protection (ICRP) have considered the appropriateness of the use of LNT for the purposes of radiation protection standard setting. The overall conclusion from both committees was that current scientific evidence remains consistent with the LNT hypothesis, while appreciating that this might not rule out the possibility that other extrapolation models might well be valid but require further evaluation and additional research to establish their validity. The dose and dose-rate effectiveness factor (DDREF) is used for adjustment in the extrapolation from high to low doses and from high to low dose rates. The BEIR VII committee proposed a new Bayesian approach for estimating DDREF and concluded that a value of 1.5 best fit the data. This is a departure from the previously used value of 2, which is still proposed by ICRP in its most recent recommendations. The current cancer risk estimation process as utilized by ICRP and BEIR VII is used here to assess the potential risks from annual whole-body computed tomography (CT) screens using information and an approach published by Brenner and Ellington. The major conclusion is that potential radiation risks need to be considered along with the pros and cons of the detection limits of the procedure and the impact of false positives.

Psychological Distress in U.S. Women Who Have Experienced False-Positive Mammograms

In the United States, approximately 10.7% of all screening mammograms lead to a false-positive result, but the overall impact of false-positives on psychological well-being is poorly understood. Data were analyzed from the 2000 U.S. National Health Interview Survey (NHIS), the most recent national survey that included a cancer control module. Study subjects were 9,755 women who ever had a mammogram, of which 1,450 had experienced a false-positive result. Psychological distress was assessed using the validated K6 questionnaire and logistic regression was used to discern any association with previous false-positive mammograms. In a multivariate analysis, women who had indicated a previous false-positive mammogram were more likely to report feeling sad (OR = 1.18, 95% CI, 1.03-1.35), restless (OR = 1.23, 95% CI, 1.08-1.40), worthless (OR = 1.27, 95% CI, 1.04-1.54), and finding that everything was an effort (OR = 1.27, 95% CI, 1.10-1.47). These women were also more likely to have seen a mental health professional in the 12 months preceding the survey (OR = 1.28, 95% CI, 1.03-1.58) and had a higher composite score on all items of the K6 scale (P < 0.0001), a reflection of increased psychological distress. Analyses by age and race revealed that, among women who had experienced false-positives, younger women were more likely to feel that everything was an effort, and blacks were more likely to feel restless. In a random sampling of the U.S. population, women who had previously experienced false-positive mammograms were more likely to report symptoms of anxiety and depression.

Quantitative assessment of filter-based cDNA microarrays: gene expression profiles of human T-lymphoma cell lines

While the use of cDNA microarrays for functional genomic analysis has become commonplace, relatively little attention has been placed on false positives, i.e. the likelihood that a change in measured radioactive or fluorescence intensity may reflect a change in gene expression when, in fact, there is none. Since cDNA arrays are being increasingly used to rapidly distinguish biomarkers for disease detection and subsequent assay development (Wellman et al., Blood, 96, 398-404, 2000), the impact of false positives can be significant. For the use of this technology, it is necessary to develop quantitative criteria for reduction of false positives with radioactively-labeled cDNA arrays. We used a single source of RNA (HuT78 T lymphoma cells) to eliminate sample variation and quantitatively examined intensity ratios using radioactively labeled cDNA microarrays. Variation in intensity ratios was reduced by processing microarrays in side-by-side (parallel mode) rather than by using the same microarray for two hybridizations (sequential mode). Based on statistical independence, calculation of the expected number of false positives as a function of threshold showed that a detection limit of [log(2)R] >0.65 with agreement from three replicates could be used to identify up- or down-modulated genes. Using this quantitative criteria, gene expression differences between two related T lymphoma cell lines, HuT78 and H9, were identified. The relevance of these findings to the known functional differences between these cell types is discussed.