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Authors who publish evaluations of dichotomous (yes/no) diagnostic tests often include the predictive values of their test at a single prior probability (eg, the prevalence of the target disease within the evaluation data set). Th...
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Authors who publish evaluations of dichotomous (yes/no) diagnostic tests often include the predictive values of their test at a single prior probability (eg, the prevalence of the target disease within the evaluation data set). The objectives of this technical note are to demonstrate why single-probability predictive values are misleading and to show a better way to display positive predictive values (PPV) and negative predictive values (NPV) for a newly evaluated test. Secondly, this technical note will show readers how to calculate predictive values from only sensitivity and specificity for any desired prior probability. As prior probability increases from 0% to 100%, PPV increases from 0% to 100%, but NPV goes in the opposite direction (drops from 100% to 0%). Because prior probabilities vary so greatly across situations, predictive values should be provided in publications for the full range of potential prior probabilities (if provided at all). This is easily done with a 2-curve graph displaying the predictive values (y-axis) against the prior probability (x-axis).
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We investigated the false-negative, true-negative, false-positive, and true-positive predictive values from a general group testing procedure for a heterogeneous population. We show that its false (true)-negative predictive value ...
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We investigated the false-negative, true-negative, false-positive, and true-positive predictive values from a general group testing procedure for a heterogeneous population. We show that its false (true)-negative predictive value of a specimen is larger (smaller), and the false (true)-positive predictive value is smaller (larger) than that from individual testing procedure, where the former is in aversion. Then we propose a nested group testing procedure, and show that it can keep the sterling characteristics and also improve the false-negative predictive values for a specimen, not larger than that from individual testing. These characteristics are studied from both theoretical and numerical points of view. The nested group testing procedure is better than individual testing on both false-positive and false-negative predictive values, while retains the efficiency as a basic characteristic of a group testing procedure. Applications to Dorfman's, Halving and Sterrett procedures are discussed. Results from extensive simulation studies and an application to malaria infection in microscopy-negative Malawian women exemplify the findings.
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Introduction: Scientists are increasingly in a position to ask whether or not to adopt new technologies. We present a visualization tool to help scientists swiftly evaluate the worth of new assays.
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BackgroundThe performance of Giardia diagnostic tests that detect either cysts or fecal antigens has not been thoroughly examined.Hypothesis/ObjectivesWe examined the concordance and agreement among 4 Giardia diagnostic tests (2 c...
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BackgroundThe performance of Giardia diagnostic tests that detect either cysts or fecal antigens has not been thoroughly examined.Hypothesis/ObjectivesWe examined the concordance and agreement among 4 Giardia diagnostic tests (2 cyst and 2 coproantigen detection methods) in a colony of dogs chronically and subclinically infected with Giardia.AnimalsTwenty dogs with chronic, subclinical Giardia infection.MethodsGiardia diagnostic tests were performed repeatedly on each dog over 120 days. Fecal cyst detection methods (ZnSO4 flotation and fluorescent antibody [FAB] coproscopy) were performed 3 times per week. Coproantigen methods (Giardia SNAP test and quantitative ELISA) were performed weekly. Results were analyzed and compared among methods.ResultsWhen compared with FAB coproscopy, all of the in-house diagnostic tests had excellent positive predictive values (PPVs, 95-99%) at the study prevalence (89%). At lower prevalence rates, ZnSO4, SNAP, and ELISA tests all had good negative predictive values (NPVs), but poor PPVs. There was poor to good agreement among tests by kappa analysis.Conclusion and Clinical RelevanceOur findings show that most commonly used in-house Giardia diagnostic tests have poor agreement with the gold standard method (FAB coproscopy). The in-house tests have good NPVs, but poor PPVs, at prevalence rates common in most clinical settings.
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BackgroundThe performance of Giardia diagnostic tests that detect either cysts or fecal antigens has not been thoroughly examined.Hypothesis/ObjectivesWe examined the concordance and agreement among 4 Giardia diagnostic tests (2 c...
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BackgroundThe performance of Giardia diagnostic tests that detect either cysts or fecal antigens has not been thoroughly examined.Hypothesis/ObjectivesWe examined the concordance and agreement among 4 Giardia diagnostic tests (2 cyst and 2 coproantigen detection methods) in a colony of dogs chronically and subclinically infected with Giardia.AnimalsTwenty dogs with chronic, subclinical Giardia infection.MethodsGiardia diagnostic tests were performed repeatedly on each dog over 120 days. Fecal cyst detection methods (ZnSO4 flotation and fluorescent antibody [FAB] coproscopy) were performed 3 times per week. Coproantigen methods (Giardia SNAP test and quantitative ELISA) were performed weekly. Results were analyzed and compared among methods.ResultsWhen compared with FAB coproscopy, all of the in-house diagnostic tests had excellent positive predictive values (PPVs, 95-99%) at the study prevalence (89%). At lower prevalence rates, ZnSO4, SNAP, and ELISA tests all had good negative predictive values (NPVs), but poor PPVs. There was poor to good agreement among tests by kappa analysis.Conclusion and Clinical RelevanceOur findings show that most commonly used in-house Giardia diagnostic tests have poor agreement with the gold standard method (FAB coproscopy). The in-house tests have good NPVs, but poor PPVs, at prevalence rates common in most clinical settings.
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The predictability of data values is studied at a fundamental level. Two basic predictor models are defined: Computational predictors perform an operation on previous values to yield predicted next value values. Examples we study ...
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The predictability of data values is studied at a fundamental level. Two basic predictor models are defined: Computational predictors perform an operation on previous values to yield predicted next value values. Examples we study are stride value prediction and last value prediction; Contest-Based predictors match recent value history (context) with previous value history and predict values based entirely on previously observed patterns. To understand the potential of value prediction we perform simulations with unbounded prediction tables that are immediately updated using correct data values.
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The aims of this study were to explore the relationship between early reduction in psychotic symptoms and the ultimate response in patients with schizophrenia treated by atypical antipsychotics, and to determine the best time to s...
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The aims of this study were to explore the relationship between early reduction in psychotic symptoms and the ultimate response in patients with schizophrenia treated by atypical antipsychotics, and to determine the best time to switch or maitain the regimen. We also explore the possible predictors for the clinical response. One hundred eleven inpatients with acutely exacerbated schizophrenia were randomized to give optimal therapy of olanzapine, risperidone, and paliperidone in one-week run-in period and 12?weeks’ intervention. All participants were assessed using Positive and Negative Syndrome Scale (PANSS). Early Response, defined as reduction of 25% in PANSS score, was examined at weeks 1, 2, 3, 4 and 8, and these ratings were used to predict ultimate response (25% PANSS reduction) at week 12. We hypothesized that early treatment response at Week 1 or 2 could predict Week 12’s treatment outcome. The early treatment response at Week 2 had a greater negative prediction value (NPV, 93.6%) than did the response at Week 1 (NPV, 69.7%), Week 3 (NPV, 91.5%), Week 4 (NPV, 90.7%) and Week 8 (NPV, 87.2%). The positive predictive value became more acceptable (65%) until Week 4. There was no any other potential predictors, including types of antipsychotics medication and treatment dosage, were associated with ultimate response in this study. The treatment non-response at Week 2 optimally predicted the ultimate (Week 12) non-response, in terms of negative predictive value (NPV). These finding suggests that the revision of treatment strategy should be considered t if patients with schizophrenia was not responsive to them after 2?weeks’ treatment, and for those who are responders at Week 2, another two weeks are needed to further evaluate whether they will be continuously responsive. NCT03730857 at ClinicalTrial.gov . Date of registration: 30/Oct/2018.
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Abstract In the area of diagnostics, it is common practice to leverage external data to augment a traditional study of diagnostic accuracy consisting of prospectively enrolled subjects to potentially reduce the time and/or cost ne...
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Abstract In the area of diagnostics, it is common practice to leverage external data to augment a traditional study of diagnostic accuracy consisting of prospectively enrolled subjects to potentially reduce the time and/or cost needed for the performance evaluation of an investigational diagnostic device. However, the statistical methods currently being used for such leveraging may not clearly separate study design and outcome data analysis, and they may not adequately address possible bias due to differences in clinically relevant characteristics between the subjects constituting the traditional study and those constituting the external data. This paper is intended to draw attention in the field of diagnostics to the recently developed propensity score‐integrated composite likelihood approach, which originally focused on therapeutic medical products. This approach applies the outcome‐free principle to separate study design and outcome data analysis and can mitigate bias due to imbalance in covariates, thereby increasing the interpretability of study results. While this approach was conceived as a statistical tool for the design and analysis of clinical studies for therapeutic medical products, here, we will show how it can also be applied to the evaluation of sensitivity and specificity of an investigational diagnostic device leveraging external data. We consider two common scenarios for the design of a traditional diagnostic device study consisting of prospectively enrolled subjects, which is to be augmented by external data. The reader will be taken through the process of implementing this approach step‐by‐step following the outcome‐free principle that preserves study integrity.
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A marker's capacity to predict the risk of a disease depends on the prevalence of disease in the target population and its accuracy of classification, i.e. its ability to discriminate diseased subjects from non-diseased subjects. ...
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A marker's capacity to predict the risk of a disease depends on the prevalence of disease in the target population and its accuracy of classification, i.e. its ability to discriminate diseased subjects from non-diseased subjects. The latter is often considered an intrinsic property of the marker; it is independent of disease prevalence and hence more likely to be similar across populations than risk prediction measures. In this paper, we are interested in evaluating the population-specific performance of a risk prediction marker in terms of the positive predictive value PPV and negative predictive value NPV at given thresholds, when samples are available from the target population as well as from another population. A default strategy is to estimate PPV and NPV using samples from the target population only. However, when the marker's accuracy of classification as characterized by a specific point on the receiver operating characteristics curve is similar across populations, borrowing information across populations allows increased efficiency in estimating PPV and NPV. We develop estimators that optimally combine information across populations. We apply this methodology to a cross-sectional study where we evaluate PCA3 as a risk prediction marker for prostate cancer among subjects with or without a previous negative biopsy.
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