Youden index and Associated Cut-points for Three Ordinal Diagnostic Groups

15 Years of Longitudinal Genetic, Clinical, Cognitive, Imaging, and Biochemical Measures in DIAN

This manuscript describes and summarizes the Dominantly Inherited Alzheimer Network Observational Study (DIAN Obs), highlighting the wealth of longitudinal data, samples, and results from this human cohort study of brain aging and a rare monogenic form of Alzheimer's disease (AD). DIAN Obs is an international collaborative longitudinal study initiated in 2008 with support from the National Institute on Aging (NIA), designed to obtain comprehensive and uniform data on brain biology and function in individuals at risk for autosomal dominant AD (ADAD). ADAD gene mutations in the amyloid protein precursor (APP), presenilin 1 (PSEN1), or presenilin 2 (PSEN2) genes are deterministic causes of ADAD, with virtually full penetrance, and a predictable age at symptomatic onset. Data and specimens collected are derived from full clinical assessments, including neurologic and physical examinations, extensive cognitive batteries, structural and functional neuro-imaging, amyloid and tau pathological measures using positron emission tomography (PET), flurordeoxyglucose (FDG) PET, cerebrospinal fluid and blood collection (plasma, serum, and whole blood), extensive genetic and multi-omic analyses, and brain donation upon death. This comprehensive evaluation of the human nervous system is performed longitudinally in both mutation carriers and family non-carriers, providing one of the deepest and broadest evaluations of the human brain across decades and through AD progression. These extensive data sets and samples are available for researchers to address scientific questions on the human brain, aging, and AD.

Models for predicting the risk of illness in leprosy contacts in Brazil: Leprosy prediction models in Brazilian contacts

OBJECTIVE

This study aims to develop and validate predictive models that assess the risk of leprosy development among contacts, contributing to an enhanced understanding of disease occurrence in this population.

METHODS

A cohort of 600 contacts of people with leprosy treated at the National Reference Center for Leprosy and Health Dermatology at the Federal University of Uberlândia (CREDESH/HC-UFU) was followed up between 2002 and 2022. The database was divided into two parts: two-third to construct the disease risk score and one-third to validate this score. Multivariate logistic regression models were used to construct the disease score.

RESULTS

Of the four models constructed, model 3, which included the variables anti-phenolic glycolipid I immunoglobulin M positive, absence of Bacillus Calmette-Guérin vaccine scar and age ≥60 years, was considered the best for identifying a higher risk of illness, with a specificity of 89.2%, a positive predictive value of 60% and an accuracy of 78%.

CONCLUSIONS

Risk prediction models can contribute to the management of leprosy contacts and the systematisation of contact surveillance protocols.

Evaluating joint confidence region of hypervolume under ROC manifold and generalized Youden index

In biomarker evaluation/diagnostic studies, the hypervolume under the receiver operating characteristic manifold (HUM K $$ {\mathrm{HUM}}_K $$ ) and the generalized Youden index (J K $$ {J}_K $$ ) are the most popular measures for assessing classification accuracy under multiple classes. WhileHUM K $$ {\mathrm{HUM}}_K $$ is frequently used to evaluate the overall accuracy,J K $$ {J}_K $$ provides direct measure of accuracy at the optimal cut-points. Simultaneous evaluation ofHUM K $$ {\mathrm{HUM}}_K $$ andJ K $$ {J}_K $$ provides a comprehensive picture about the classification accuracy of the biomarker/diagnostic test under consideration. This article studies both parametric and non-parametric approaches for estimating the confidence region ofHUM K $$ {\mathrm{HUM}}_K $$ andJ K $$ {J}_K $$ for a single biomarker. The performances of the proposed methods are investigated by an extensive simulation study and are applied to a real data set from the Alzheimer's Disease Neuroimaging Initiative.

Development of complexity categories for an investigational drug services complexity scoring tool to assess pharmacy effort in clinical trial initiation and maintenance

PURPOSE

Research pharmacy effort required to safely and compliantly manage investigational products (IP) varies between studies. No validated tool exists in the United States to evaluate these differences in effort. The Vizient Pharmacy Research Committee Investigational Drug Services (IDS) Subcommittee previously developed a systematic complexity scoring tool (CST) through expert consensus to assign a complexity score for pharmacy effort. This project seeks to develop and validate complexity categories based on CST scores.

METHODS

Vizient member institutions in IDS assigned a CST complexity score and a perceived complexity category (low, medium, or high) for study initiation and maintenance. Receiver operating characteristic (ROC) curve analysis defined the best CST score cutoff points for each complexity category. Comparing the CST-assigned to the user-perceived complexity category determined whether the CST-assigned complexity category aligned with practitioner assignment.

RESULTS

A total of 322 responses were used to determine complexity score categories. The AUC values for study initiation and maintenance were 0.79 (P < 0.001) for the low/medium boundary and 0.80 (P < 0.001) for the medium/high boundary, suggesting the performance of the CST is good. The agreement between CST-assigned and user-perceived complexity categories was 60% for study initiation and 58% for maintenance. The Kendall rank correlation coefficient between the raters and ROC categories was strong, with a value of 0.48 for study initiation and 0.47 for maintenance.

CONCLUSION

Development of the CST allows IDS pharmacies to objectively measure the complexity of clinical trials, which is a significant step towards assessing workload and guiding resource allocation.

Prognosticating Mortality of Primary Cardiogenic Shock Requiring Extracorporeal Life Support: The RESCUE Score

We aimed to identify prognostic laboratory markers during extracorporeal life support (ECLS) in patients with primary refractory cardiogenic shock (RCS) and to create a preliminary specific mortality score. All 208 consecutive subjects admitted for primary RCS and treated with ECLS between January-2009 and December-2018 were retrospectively analyzed. Multivariate regression analysis on laboratory markers during the first nine days of ECLS was used to develop a "Refractory End-stage Shock CUred with Ecls" (RESCUE) score. Serum creatinine (OR = 3.72, 95%CI: 2.01-6.88), direct bilirubin (OR = 1.40, 95%CI: 1.05-1.8), and platelet count (OR = 0.62, 95%CI: 0.42-0.94) were independent predictors of in-hospital mortality and were included in the score. The mean AUC was 0.763 (95%CI: 0.698-0.828) in the development cohort and 0.729 (95%CI: 0.664-0.794) in the bootstrap internal validation cohort. The RESCUE score represents a novel promising instrument to predict early mortality during the first critical days of ECLS and to help in properly guiding the therapeutic decision-making process.

On optimal biomarker cutoffs accounting for misclassification costs in diagnostic trilemmas with applications to pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is the most deadly cancer and currently there is strong clinical interest in novel biomarkers that contribute to its early detection. Assessing appropriately the accuracy of such biomarkers is a crucial issue and often one needs to take into account that many assays include biospecimens of individuals coming from three groups: healthy, chronic pancreatitis, and PDAC. The ROC surface is an appropriate tool for assessing the overall accuracy of a marker employed under such trichotomous settings. A decision/classification rule is often based on the so-called Youden index and its three-dimensional generalization. However, both the clinical and the statistical literature have not paid the necessary attention to the underlying false classification (FC) rates that are of equal or even greater importance. In this article we provide a framework to make inferences around all classification rates as well as comparisons. We explore the trinormal model, flexible models based on power transformations, and robust non-parametric alternatives. We provide a full framework for the construction of confidence intervals, regions, and spaces for joint inferences or for clinically meaningful points of interest. We further discuss the implications of costs related to different FCs. We evaluate our approaches through extensive simulations and illustrate them using data from a recent PDAC study conducted at the MD Anderson Cancer Center.

Construction of joint confidence spaces for the optimal true class fraction triplet in the ROC space using alternative biomarker cutoffs

Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver. Finding new biomarkers for its early detection is of high clinical importance. As with many other diseases, cancer has a progressive nature. In cancer biomarker studies, it is often the case that the true disease status of the recruited individuals exhibits more than two classes. The receiver operating characteristic (ROC) surface is a well-known statistical tool for assessing the biomarkers' discriminatory ability in trichotomous settings. The volume under the ROC surface (VUS) is an overall measure of the discriminatory ability of a marker. In practice, clinicians are often in need of cutoffs for decision-making purposes. A popular approach for computing such cutoffs is the Youden index and its recent three-class generalization. A drawback of such a method is that it treats the data in a pairwise fashion rather than consider all the data simultaneously. The use of the minimized Euclidean distance from the perfection corner to the ROC surface (also known as closest to perfection method) is an alternative to the Youden index that may be preferable in some settings. When such a method is employed, there is a need for inferences around the resulting true class rates/fractions that correspond to the optimal operating point. In this paper, we provide an inferential framework for the derivation of marginal confidence intervals (CIs) and joint confidence spaces (CSs) around the corresponding true class fractions, when dealing with trichotomous settings. We explore parametric and nonparametric approaches for the construction of such CIs and CSs. We evaluate our approaches through extensive simulations and apply them to a real data set that refers to HCC patients.

Receiver operating characteristic estimation and threshold selection criteria in three-class classification problems for clustered data

Statistical evaluation of diagnostic tests, and, more generally, of biomarkers, is a constantly developing field, in which complexity of the assessment increases with the complexity of the design under which data are collected. One particularly prevalent type of data is clustered data, where individual units are naturally nested into clusters. In these cases, Bias can arise from omission, in the evaluation process, of cluster-level effects and/or individual covariates. Focusing on the three-class case and for continuous-valued diagnostic tests, we investigate how to exploit the clustered structure of data within a linear-mixed model approach, both when the assumption of normality holds and when it does not. We provide a method for the estimation of covariate-specific receiver operating characteristic surfaces and discuss methods for the choice of optimal thresholds, proposing three possible estimators. A proof of consistency and asymptotic normality of the proposed threshold estimators is given. All considered methods are evaluated by extensive simulation experiments. As an application, we study the use of the Lysosomal Associated Membrane Protein Family Member 5 gene expression as a biomarker to distinguish among three types of glutamatergic neurons.

Increased Surgical Site Subcutaneous Fat Thickness Is Associated with Infection after Posterior Cervical Fusion

Background: Previous literature has associated increased body mass index (BMI) with risk of surgical site infection (SSI) after posterior cervical fusion (PCF) surgery. However, few studies have examined the association between local adiposity and risk of SSI, re-admission, and re-operation after PCF. Local adiposity is easily measured on pre-operative magnetic resonance imaging (MRI) and may act as a more accurate predictor compared with BMI. Patients and Methods: Subjects undergoing PCF from 2013-2018 at a single institution were identified retrospectively. Posterior cervical subcutaneous fat thickness, paraspinal muscle thickness, and lamina-to-skin distance measurements were obtained from computed tomography (CT) or MRI scans. Subjects with active infection, malignancy, or revision procedures were excluded. Results: Two hundred five patients were included with 20 developing SSIs. Subjects with SSIs had a longer fusion construct (4.90 vs. 3.71 levels; p = 0.001), higher Elixhauser comorbidity index (ECI; 2.05 vs. 1.34; p = 0.045), had a history of diabetes mellitus (30% vs. 10.8%; p = 0.026), higher subcutaneous fat thickness (30.5 vs. 23.6 mm; p = 0.013), and higher lamina-to-skin distance (66.4 vs. 57.9 mm; p = 0.027). Subcutaneous fat thickness (odds ratio [OR], 1.05; 95% confidence interval [CI], 1.01-1.10]; p = 0.026) and lamina-to-skin distance (OR, 1.05; 95% CI, 1.01-1.09]; p = 0.014) were associated with SSI in multivariable analysis. A subcutaneous fat thickness cutoff value of 23.2 mm had 90% sensitivity and 54.1% specificity for prediction of SSI. There was no association need for re-admission or re-operation. Conclusions: Increased posterior cervical fat may increase the risk of SSI after PCF. Pre-operative advanced imaging may be a valuable tool for assisting with patient counseling, optimization, and risk stratification.

Measuring emotional preoperative stress by an app approach and its applicability to predict postoperative pain

BACKGROUND

The Brief Measure of Emotional Preoperative Stress (B-MEPS) was developed to evaluate the preoperative individual vulnerability to emotional stress. To obtain a refined version of B-MEPS suitable for an app approach, this study aimed: (i) to identify items with more discriminant properties; (ii) to classify the level of preoperative emotional stress based on cut-off points; (iii) to assess concurrent validity through correlation with the Central Sensitization Inventory (CSI) score; (iv) to confirm whether the refined version of B-MEPS is an adequate predictive measure for identification of patients prone to intense postoperative pain.

METHODS

We include 1016 patients who had undergone surgical procedures in a teaching hospital. The generalized partial credit model of item response theory and latent class model were employed, respectively, to reduce the number of items and to create cut-off points. We applied the CSI and assessed pain by Visual Analog Scale (0-10) and by the amount of postoperative morphine consumption.

RESULTS

The refined B-MEPS shows satisfactory reliability (Cronbach's alpha 0.79). Preoperative emotional stress, according to the cut-off points, is classified into categories: low, intermediate or high stress. The refined B-MEPS exhibited a linear association with the CSI scores (r2 = 0.53, p < 0.01). Patients with higher levels of emotional stress displayed a positive association with moderate to severe pain and greater morphine consumption.

CONCLUSION

The refined version of B-MEPS, along with an interface of easy applicability, assess emotional vulnerability at the bedside before surgery. This app may support studies focused on intervening with perioperative stress levels.

A family of estimators to diagnostic accuracy when candidate tests are subject to detection limits-Application to diagnosing early stage Alzheimer disease

In disease diagnosis, individuals are usually assumed to be one of the two basic types, healthy or diseased, as typically based on an established gold standard. Candidate markers for diagnosing a disease often are much cheaper and less invasive than the gold standard but must be evaluated against the gold standard for their sensitivity and specificity to accurately diagnose the disease. When candidate diagnostic markers are fully measured, receiver operating characteristic curves have been the standard approaches for assessing diagnostic accuracy. However, full measurements of diagnostic markers may not be available above or below certain limits due to various practical and technical limitations. For example, in the diagnosis of Alzheimer disease using cerebrospinal fluid biomarkers, the Roche Elecsys® immunoassays have a measuring range for multiple cerebrospinal fluid molecular concentrations. Many cognitive tests used in diagnosing dementia due to Alzheimer disease are also subject to detection limits, often referred to as the floor and ceiling effects in the neuropsychological literature. We propose a new statistical methodology for estimating the diagnostic accuracy when a diagnostic marker is subject to detection limits by dividing the entire study sample into two sub-samples by a threshold of the diagnostic marker. We then propose a family of estimators to the area under the receiver operating characteristic curve by combining a conditional nonparametric estimator and another conditional semi-parametric estimator derived from Cox's proportional hazards model. We derive the variance to the proposed estimators, and further, assess the performance of the proposed estimators as a function of possible thresholds through an extensive simulation study, and recommend the optimum thresholds. Finally, we apply the proposed methodology to assess the ability of several cerebrospinal fluid biomarkers and cognitive tests in diagnosing early stage Alzheimer disease dementia.

Scalable Bio Marker Combinations for Early Stroke Diagnosis: A Systematic Review

Background: Acute stroke treatment is a time-critical process in which every minute counts. Laboratory biomarkers are needed to aid clinical decisions in the diagnosis. Although imaging is critical for this process, these biomarkers may provide additional information to distinguish actual stroke from its mimics and monitor patient condition and the effect of potential neuroprotective strategies. For such biomarkers to be effectively scalable to public health in any economic setting, these must be cost-effective and non-invasive. We hypothesized that blood-based combinations (panels) of proteins might be the key to this approach and explored this possibility through a systematic review. Methods: We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines for systematic review. Initially, the broader search for biomarkers for early stroke diagnosis yielded 704 hits, and five were added manually. We then narrowed the search to combinations (panels) of the protein markers obtained from the blood. Results: Twelve articles dealing with blood-based panels of protein biomarkers for stroke were included in the systematic review. We observed that NR2 peptide (antibody against the NR2 fragment) and glial fibrillary acidic protein (GFAP) are brain-specific markers related to stroke. Von Willebrand factor (vWF), matrix metalloproteinase 9 (MMP-9), and S100β have been widely used as biomarkers, whereas others such as the ischemia-modified albumin (IMA) index, antithrombin III (AT-III), and fibrinogen have not been evaluated in combination. We herein propose the following new combination of biomarkers for future validation: panel 1 (NR2 + GFAP + MMP-9 + vWF + S100β), panel 2 (NR2 + GFAP + MMP-9 + vWF + IMA index), and panel 3 (NR2 + GFAP + AT-III + fibrinogen). Conclusions: More research is needed to validate, identify, and introduce these panels of biomarkers into medical practice for stroke recurrence and diagnosis in a scalable manner. The evidence indicates that the most promising approach is to combine different blood-based proteins to provide diagnostic precision for health interventions. Through our systematic review, we suggest three novel biomarker panels based on the results in the literature and an interpretation based on stroke pathophysiology.

Gut vascular barrier impairment leads to intestinal bacteria dissemination and colorectal cancer metastasis to liver

Metastasis is facilitated by the formation of a "premetastatic niche," which is fostered by primary tumor-derived factors. Colorectal cancer (CRC) metastasizes mainly to the liver. We show that the premetastatic niche in the liver is induced by bacteria dissemination from primary CRC. We report that tumor-resident bacteria Escherichia coli disrupt the gut vascular barrier (GVB), an anatomical structure controlling bacterial dissemination along the gut-liver axis, depending on the virulence regulator VirF. Upon GVB impairment, bacteria disseminate to the liver, boost the formation of a premetastatic niche, and favor the recruitment of metastatic cells. In training and validation cohorts of CRC patients, we find that the increased levels of PV-1, a marker of impaired GVB, is associated with liver bacteria dissemination and metachronous distant metastases. Thus, PV-1 is a prognostic marker for CRC distant recurrence and vascular impairment, leading to liver metastases.

Natural History of Functional Tricuspid Regurgitation Quantified by Cardiovascular Magnetic Resonance

BACKGROUND

Quantitation of tricuspid regurgitant (TR) severity can be challenging with conventional echocardiographic imaging and may be better evaluated using cardiovascular magnetic resonance (CMR).

OBJECTIVES

In patients with functional TR, this study sought to examine the relationship between TR volume (TRVol) and TR fraction (TRF) with all-cause mortality.

METHODS

We examined 547 patients with functional TR using CMR to quantify TRVol and TRF. The primary outcome was all-cause mortality. Thresholds for mild, moderate, and severe TR were derived based on natural history outcome data.

RESULTS

During a median follow-up of 2.6 years (interquartile range: 1.7 to 3.3 years), there were 93 deaths, with an estimated 5-year survival of 79% (95% confidence interval [CI]: 73% to 83%). After adjustment of clinical and imaging variables, including RV function, both TRF (adjusted hazard ratio [AHR] per 10% increment: 1.26; 95% CI: 1.10 to 1.45; p = 0.001) and TRVol (AHR per 10-ml increment: 1.15; 95% CI: 1.04 to 1.26; p = 0.004) were associated with mortality. Patients in the highest-risk strata of TRVol ≥45 ml or TRF ≥50% had the worst prognosis (AHR: 2.26; 95% CI: 1.36 to 3.76; p = 0.002 for TRVol and AHR: 2.60; 95% CI: 1.45 to 4.66; p = 0.001 for TRF).

CONCLUSIONS

This is the first study to use CMR to assess independent prognostic implications of functional TR. Both TRF and TRVol were associated with increased mortality after adjustment for clinical and imaging covariates, including right ventricular ejection fraction. A TRVol of ≥45 ml or TRF of ≥50% identified patients in the highest-risk strata for mortality. These CMR thresholds should be used for patient selection in future trials to determine if tricuspid valve intervention improves outcomes in this high-risk group.

Overexpressed PLAU and its potential prognostic value in head and neck squamous cell carcinoma

Background

Metastasis is a major event for survival and prognosis in patients with head and neck squamous cell carcinomas (HNSCC). A primary cause of metastasis is the proteolytic degradation of the extracellular matrix (ECM). The plasminogen activator urokinase (PLAU) is involved in the transformation of plasminogen to plasmin leading to hydrolyzation of ECM-related proteins. However, the role of PLAU expression in HNSCC is unclear and the worth being investigated.

Methods

PLAU expression profiles and clinical parameters from multiple HNSCC datasets were used to investigate the relationship of PLAU expression and HNSCC survival. GO and PPI network were established on PLAU-related downstream molecular. The stroma score was deconvoluted for analysis of PLAU’s association with the immune environment. ROC analysis was applied to show the performance of PLAU in predicting HNSCC prognosis.

Results

PLAU mRNA was significantly elevated, as opposed to its methylation, in HNSCC tumor samples over normal specimens (all p < 0.01). Univariate and multivariate cox analysis showed PLAU could be an independent indicator for HNSCC prognosis. Combining with neck lymph node status, the AUC of PLAU in predicting 5-years overall survival reached to 0.862. GO enrichment analysis showed the major biological process (extracellular matrix organization and the P13K-Akt signaling pathway) may involve to the possible mechanism of PLAU’s function on HNSCC prognosis. Furthermore, PLAU expression was positively correlated with stroma cell score, M1 type macrophages, and negatively associated with CD4 + T cell, Tregs cell, and follicular helper T cell.

Conclusions

PLAU might be an independent biomarker for predicting outcomes of HNSCC patients. The elevated expression of PLAU was associated with HPV positivity and neck node status. The PI3K-Akt pathway and aberrant proportions of immune cells might underly the mechanism of PLAU’s oncogene role in HNSCC.

Comparisons of conventional and novel anthropometric obesity indices to predict metabolic syndrome among vegetarians in Malaysia

Our study aimed to compare the ability of anthropometric obesity indices to predict MetS and to determine the sex-specific optimal cut-off values for MetS among Malaysian vegetarians. Body weight, height, waist circumference (WC), blood pressure (BP), fasting venous blood sample were collected from 273 vegetarians in Selangor and Kuala Lumpur, Malaysia. The abilities of body mass index (BMI), body fat percentage (BF%), waist to height ratio (WHtR), lipid accumulation product (LAP), visceral adiposity index (VAI), a body shape index (ABSI), and body roundness index (BRI) to identify MetS were tested using receiver operating characteristic (ROC) curve analyses. MetS was defined according to the Joint Interim Statement 2009. The ROC curve analyses show that BMI, BF%, WHtR, LAP and VAI were able to discriminate MetS in both sexes. LAP was a better predictor to predict MetS, followed by WHtR for male and female vegetarians. The suggested WHtR’s optimal cut-offs and LAP’s optimal cut-offs for MetS for male and female vegetarians were 0.541, 0.532, 41.435 and 21.743, respectively. In conclusion, LAP was a better predictor to predict MetS than other anthropometric obesity indices. However, WHtR could be an alternative obesity index in large epidemiology survey due to its convenient and cost-effective characteristics.

Item response theory analysis of the Clinical Dementia Rating

INTRODUCTION

The Clinical Dementia Rating (CDR) is widely used in Alzheimer's disease research studies and has well established reliability and validity. To facilitate the development of an online, electronic CDR (eCDR) for more efficient clinical applications, this study aims to produce a shortened version of the CDR, and to develop the statistical model for automatic scoring.

METHODS

Item response theory (IRT) was used for item evaluation and model development. An automatic scoring algorithm was validated using existing CDR global and domain box scores as the reference standard.

RESULTS

Most CDR items discriminate well at mild and very mild levels of cognitive impairment. The bi-factor IRT model fits best and the shortened CDR still demonstrates very high classification accuracy (81%∼92%).

DISCUSSION

The shortened version of the CDR and the automatic scoring algorithm has established a good foundation for developing an eCDR and will ultimately improve the efficiency of cognitive assessment.

Tracing Autism Traits in Large Multiplex Families to Identify Endophenotypes of the Broader Autism Phenotype

Families comprising many individuals with Autism Spectrum Disorders (ASD) may carry a dominant predisposing mutation. We implemented rigorous phenotyping of the "Broader Autism Phenotype" (BAP) in large multiplex ASD families using a novel endophenotype approach for the identification and characterisation of distinct BAP endophenotypes. We evaluated ASD/BAP features using standardised tests and a semi-structured interview to assess social, intellectual, executive and adaptive functioning in 110 individuals, including two large multiplex families (Family A: 30; Family B: 35) and an independent sample of small families (n = 45). Our protocol identified four distinct psychological endophenotypes of the BAP that were evident across these independent samples, and showed high sensitivity (97%) and specificity (82%) for individuals classified with the BAP. Patterns of inheritance of identified endophenotypes varied between the two large multiplex families, supporting their utility for identifying genes in ASD.

Augmentation of HIV-specific T cell function by immediate treatment of hyperacute HIV-1 infection

Sustained viremia after acute HIV infection is associated with profound CD4⁺ T cell loss and exhaustion of HIV-specific CD8⁺ T cell responses. To determine the impact of combination antiretroviral therapy (cART) on these processes, we examined the evolution of immune responses in acutely infected individuals initiating treatment before peak viremia. Immediate treatment of Fiebig stages I and II infection led to a rapid decline in viral load and diminished magnitude of HIV-specific (tetramer⁺) CD8⁺ T cell responses compared to untreated donors. There was a strong positive correlation between cumulative viral antigen exposure before full cART-induced suppression and immune responses measured by MHC class I tetramers, IFN-γ ELISPOT, and CD8⁺ T cell activation. HIV-specific CD8⁺ T responses of early treated individuals were characterized by increased CD127 and BCL-2 expression, greater in vitro IFN-γ secretion, and enhanced differentiation into effector memory (T_em) cells. Transcriptional analysis of tetramer⁺ CD8⁺ T cells from treated persons revealed reduced expression of genes associated with activation and apoptosis, with concurrent up-regulation of prosurvival genes including BCL-2, AXL, and SRC Early treatment also resulted in robust HIV-specific CD4⁺ T cell responses compared to untreated HIV-infected individuals. Our data show that limiting acute viremia results in enhanced functionality of HIV-specific CD4⁺ and CD8⁺ T cells, preserving key antiviral properties of these cells.

Statistical Methodology for Multiclass Classifications: Applications to Dementia

Alzheimer's disease (AD) is a common, devastating disease which carries a heavy economic burden. Accelerated efforts to identify presymptomatic stages of AD and biomarkers to classify the disease are urgent needs. Currently, no biomarkers can perfectly discriminate individuals into multiple disease categories of AD (no cognitive impairment, mild cognitive impairment, and dementia). Although many biomarkers for diagnosis and their various features are being studied, we lack advanced statistical methods which can fully utilize biomarkers to classify AD accurately, thereby facilitating evaluation of putative markers both alone and in combination. In this paper, we propose two approaches: 1) a forward addition procedure in which we adapt an additive logistic regression model to the setting for disease with ordered multiple categories. Using this approach, we select and combine multiple cross-sectional biomarkers to improve diagnostic accuracy, and 2) a method by extending the Neyman-Pearson Lemma to the ordered three disease categories to construct optimal cutoff points to distinguish multiple disease categories. We evaluate the robustness of the proposed model using a simulation study. Then we apply these two methods to data from the Religious Orders Study to examine the feasibility of combining biomarkers, and compare the diagnostic accuracy between the proposed methods and existing methods including model-based methods (ordinal logistic regression and quadratic discriminant analysis), a tree-based method CART, and the Youden index method. The two proposed methods facilitate evaluations of biomarkers for conditions with graded, rather than binary, classifications. The evaluation of the performance of different approaches provides guidance of how to choose approaches to address research questions.

Sputum long non-coding RNA biomarkers for diagnosis of lung cancer

BACKGROUND

Analysis of molecular changes in sputum may help diagnose lung cancer. Long non-coding RNAs (lncRNAs) play vital roles in various biological processes, and their dysregulations contribute to the development and progression of lung tumorigenesis. Herein, we determine whether aberrant lncRNAs could be used as potential sputum biomarkers for lung cancer.

METHODS

Using reverse transcription PCR, we measure expressions of lung cancer-associated lncRNAs in sputum of a discovery cohort of 67 lung cancer patients and 65 cancer-free smokers with benign diseases and a validation cohort of 59 lung cancer patients and 60 cancer-free smokers with benign diseases.

RESULTS

In the discovery cohort, four of the lncRNAs displayed a significantly different level in sputum of lung cancer patients vs.cancer-free smokers with benign diseases (all P< 0.001). From the four lncRNAs, three lncRNAs (SNHG1, H19, and HOTAIR) are identified as a biomarker panel, producing 82.09% sensitivity and 89.23% specificity for diagnosis of lung cancer. Furthermore, the biomarker panel has a higher sensitivity (82.09% vs. 52.24%, P= 0.02) and a similar specificity compared with sputum cytology (89.23% vs. 90.77%, P= 0.45). In addition, the lncRNA biomarker panel had a higher sensitivity (87.50% vs. 70.07%, p= 0.03) for diagnosis of squamous cell carcinoma compared with adenocarcinoma of the lung, while maintaining the same specificity (89.23%). The potential of the sputum lncRNA biomarkers for lung cancer detection is confirmed in the validation cohort.

CONCLUSION

We have for the first time shown that the analysis of lncRNAs in sputum might be a noninvasive approach for diagnosis of lung cancer.

The relationship between platelet count and bone mineral density: results from two independent population-based studies

In two population-based study of middle-aged and older people, we investigated if platelet count was associated with bone mineral density and determined whether the association remained over time. Highest platelet counts within the normal range are significantly associated with osteopenia and osteoporosis in middle-aged and elderly people.

PURPOSE

Recently, platelets were found to play a role in bone remodeling. However, data on the association between platelet count and osteoporosis are lacking. Our study aimed to investigate the association between platelet counts, osteopenia, and osteoporosis in middle-aged and elderly Koreans.

METHODS

We analyzed cross-sectional data from 5181 adults (postmenopausal women and men over 50 years of age) in the 2008-2011 Korea National Health and Nutrition Examination Survey (KNHANES) and longitudinal prospective data from 3312 adults over 50 years of age in the Korean Genome and Epidemiology Study (KoGES). Bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry in the KNHANES and quantitative ultrasound in the KoGES. The platelet counts were categorized into quintiles within normal ranges (150-450 × 10³ μL). The associations between platelet counts, osteopenia, and osteoporosis were estimated using a multinomial logistic model.

RESULTS

BMD of the femur neck, total femur, and lumbar spine all decreased with increasing platelet counts. The cut-off points of the platelet counts to differentiate normal BMD from osteopenia and osteopenia from osteoporosis were 217 × 10³/μL and 269 × 10³/μL, respectively. The odds ratios (95% confidence intervals) in the highest platelet quartile were 1.39 (1.03-1.88) for osteopenia and 1.60 (1.07-2.37) for osteoporosis after adjusting for confounding factors. The distal radius T-score was significantly decreased in the highest platelet tertile group at a follow-up of 10 years.

CONCLUSION

Highest platelet counts within the normal range are significantly associated with osteopenia and osteoporosis in middle-aged and elderly people.

Analysis of the Metabolic Characteristics of Serum Samples in Patients With Multiple Myeloma

Aims: This study aimed to identify potential, non-invasive biomarkers for diagnosis and monitoring of the progress in multiple myeloma (MM) patients. Methods: MM patients and age-matched healthy controls (HC) were recruited in Discovery phase and Validation phase, respectively. MM patients were segregated into active group (AG) and responding group (RG). Serum samples were collected were conducted to non-targeted metabolomics analyses. Metabolites which were significantly changed (SCMs) among groups were identified in Discovery phase and was validated in Validation phase. The signaling pathways of these SCMs were enriched. The ability of SCMs to discriminate among groups in Validation phase was analyzed through receiver operating characteristic curve. The correlations between SCMs and clinical features, between SCMs and survival period of MM patients were analyzed. Results: Total of 23 SCMs were identified in AG compared with HC both in Discovery phase and Validation phase. Those SCMs were significantly enriched in arginine and proline metabolism and glycerophospholipid metabolism. 4 SCMs had the discriminatory ability between MM patients and healthy controls in Validation phase. Moreover, 12 SCMs had the ability to discriminate between the AG patients and RG patients in Validation phase. 10 out of 12 SCMs correlated with advanced features of MM. Moreover, 8 out of 12 SCMs had the negative impact on the survival of MM. 5'-Methylthioadenosine may be the only independent prognostic factor in survival period of MM. Conclusion: 10 SCMs identified in our study, which correlated with advanced features of MM, could be potential, novel, non-invasive biomarkers for active disease in MM.

Serum IL-6 in combination with synovial IL-6/CRP shows excellent diagnostic power to detect hip and knee prosthetic joint infection

BACKGROUND

The diagnosis of prosthetic joint infection (PJI) is still a challenge in some patients after total joint replacement. Interleukin-6 (IL-6) strongly participates in the arrangement of the host-bacteria response. Therefore, increased levels of IL-6 should accompany every PJI.

PURPOSE

The aim of the study was to show diagnostic characteristics of serum IL-6 for the diagnosis of prosthetic joint infection (PJI). We also compared the diagnostic values of serum IL-6 with synovial IL-6 (sIL-6) and synovial C-reactive protein (sCRP).

STUDY DESIGN

We performed a prospective study of 240 patients in whom serum IL-6 was determined before total hip (n = 124) or knee (n = 116) reoperations. The PJI diagnosis was based on the MSIS (Musculoskeletal Infection Society) criteria (2011). Receiver operating characteristic plots were constructed for IL-6, sIL-6, and sCRP.

RESULTS

PJI was diagnosed in 93 patients, and aseptic revision was diagnosed in 147 patients. The AUC (area under curve) for IL-6 was 0.938 (95% CI; 0.904-0.971). The optimal IL-6 cut-off value for PJI was 12.55 ng/L. Positive and negative likelihood ratios for IL-6 were 8.24 (95% CI; 4.79-14.17) and 0.15 (95% CI; 0.09-0.26), respectively. The optimal sIL-6 and sCRP cut-off values were 20,988 ng/L and 8.80 mg/L, respectively. Positive and negative likelihood ratios for sIL-6 were 40.000 (95% CI; 5.7-280.5) and 0.170 (95% CI; 0.07-0.417), respectively. Negative likelihood ratio for sCRP was 0.083 (95% CI; 0.022-0.314).

CONCLUSIONS

The present study identified the cut-off values for serum/synovial IL-6 and synovial CRP for diagnostics of PJI at the site of THA and TKA and separately for each site. The diagnostic odds ratio for serum/synovial IL-6 and synovial CRP is very good. Simultaneous positivity of serum IL-6 either with synovial IL-6 or synovial CRP almost excludes false negative detection of PJI at the site of interest.

Accuracy of Susceptibility-Weighted Imaging and Dynamic Susceptibility Contrast Magnetic Resonance Imaging for Differentiating High-Grade Glioma from Primary Central Nervous System Lymphomas: Meta-Analysis

BACKGROUND AND PURPOSE

It is always a great challenge to distinguish high-grade glioma (HGG) from primary central nervous system lymphoma (PCNSL). We conducted a meta-analysis to assess the performance of dynamic susceptibility contrast (DSC) and susceptibility-weighted imaging (SWI) in differentiating HGGs from PCNSLs.

METHODS

Heterogeneity and threshold effect were evaluated, and sensitivity, specificity (SPE), and areas under the summary receiver operating characteristic curve (SROC) were calculated.

RESULTS

Thirteen studies with a total of 704 participants were included in this meta-analysis. For the dynamic susceptibility contrast (DSC) technique, the pooled sensitivity and SPE were 0.94 and 0.84, respectively. The area under the curve (AUC) under the SROC was 0.9595, and the diagnostic odds ratio was 81.880. For the susceptibility weighted imaging (SWI) technique, the pooled sensitivity and SPE were 0.92 and 0.87, respectively. The AUC under the SROC was 0.9608, and the diagnostic odds ratio was 89.774. A Z test showed that no significant difference in AUC values between DSC and SWI (P = 0.97) was observed. In addition, no obvious public bias was observed in both the DSC (P = 0.69) and SWI (P = 0.32) groups.

CONCLUSIONS

This meta-analysis indicated that both DSC-MRI and SWI have a high and similar level of diagnostic accuracy for differentiating HGGs from PCNSLs.

Integrating Circulating Immunological and Sputum Biomarkers for the Early Detection of Lung Cancer

We have demonstrated that assessments of microRNA (miRNA) expressions in circulating peripheral blood mononucleated cell (PBMC) and sputum specimens, respectively, may help diagnose lung cancer. To assess the individual and combined analysis of the miRNAs across the different body fluids for lung cancer early detection, we analyse a panel of 3 sputum miRNAs (miRs-21, 31, and 210) and a panel of 2 PBMC miRNAs (miRs-19b-3p and 29b-3p) in a discovery cohort of 68 patients with lung cancer and 66 cancer-free smokers. We find that integrating 2 sputum miRNAs (miRs-31 and 210) and 1 PBMC miRNA (miR-19b-3p) has higher sensitivity (86.8%) and specificity (92.4%) compared with the individual panels. The synergistic value of the integrated panel of 3 biomarkers is confirmed in a validation cohort, independent of stage and histological type of lung cancer, and patients' age, sex, and ethnicity. Integrating circulating immunological and sputum biomarkers could improve the early detection of lung cancer.

Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS) Severity

In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven) regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to) different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS) disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia), Mathematics (Dyscalculia), or Writing (Dysgraphia). By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

Bayesian nonparametric inference for the three-class Youden index and its associated optimal cutoff points

The three-class Youden index serves both as a measure of medical test accuracy and a criterion to choose the optimal pair of cutoff values for classifying subjects into three ordinal disease categories (e.g. no disease, mild disease, advanced disease). We present a Bayesian nonparametric approach for estimating the three-class Youden index and its corresponding optimal cutoff values based on Dirichlet process mixtures, which are robust models that can handle intricate features of distributions for complex data. Results from a simulation study are presented and an application to data from the Trail Making Test to assess cognitive impairment in Parkinson's disease patients is detailed.

Confidence intervals for differences between volumes under receiver operating characteristic surfaces (VUS) and generalized Youden indices (GYIs)

This article explores both existing and new methods for the construction of confidence intervals for differences of indices of diagnostic accuracy of competing pairs of biomarkers in three-class classification problems and fills the methodological gaps for both parametric and non-parametric approaches in the receiver operating characteristic surface framework. The most widely used such indices are the volume under the receiver operating characteristic surface and the generalized Youden index. We describe implementation of all methods and offer insight regarding the appropriateness of their use through a large simulation study with different distributional and sample size scenarios. Methods are illustrated using data from the Alzheimer's Disease Neuroimaging Initiative study, where assessment of cognitive function naturally results in a three-class classification setting.

The performance of MR perfusion-weighted imaging for the differentiation of high-grade glioma from primary central nervous system lymphoma: A systematic review and meta-analysis

It is always a great challenge to distinguish high-grade glioma (HGG) from primary central nervous system lymphoma (PCNSL). We conducted a meta-analysis to assess the performance of MR perfusion-weighted imaging (PWI) in differentiating HGG from PCNSL. The heterogeneity and threshold effect were evaluated, and the sensitivity (SEN), specificity (SPE) and areas under summary receiver operating characteristic curve (SROC) were calculated. Fourteen studies with a total of 598 participants were included in this meta-analysis. The results indicated that PWI had a high level of accuracy (area under the curve (AUC) = 0.9415) for differentiating HGG from PCNSL by using the best parameter from each study. The dynamic susceptibility-contrast (DSC) technique might be an optimal index for distinguishing HGGs from PCNSLs (AUC = 0.9812). Furthermore, the DSC had the best sensitivity 0.963 (95%CI: 0.924, 0.986), whereas the arterial spin-labeling (ASL) displayed the best specificity 0.896 (95% CI: 0.781, 0.963) among those techniques. However, the variability of the optimal thresholds from the included studies suggests that further evaluation and standardization are needed before the techniques can be extensively clinically used.

A nonparametric fiducial interval for the Youden index in multi-state diagnostic settings

The Youden index is a commonly employed metric to characterize the performance of a diagnostic test at its optimal point. For tests with three or more outcome classes, the Youden index has been extended; however, there are limited methods to compute a confidence interval (CI) about its value. Often, outcome classes are assumed to be normally distributed, which facilitates computational formulas for the CI bounds; however, many scenarios exist for which these assumptions cannot be made. In addition, many of these existing CI methods do not work well for small sample sizes. We propose a method to compute a nonparametric interval about the Youden index utilizing the fiducial argument. This fiducial interval ensures that CI coverage is met regardless of sample size, underlying distributional assumptions, or use of a complex classifier for diagnosis. Two alternate fiducial intervals are also considered. A simulation was conducted, which demonstrates the coverage and interval length for the proposed methods. Comparisons were made using no distributional assumptions on the outcome classes and for when outcomes were assumed to be normally distributed. In general, coverage probability was consistently met, and interval length was reasonable. The proposed fiducial method was also demonstrated in data examining biomarkers in subjects to predict diagnostic stages ranging from normal kidney function to chronic allograph nephropathy. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

A new diagnostic accuracy measure and cut-point selection criterion

Most diagnostic accuracy measures and criteria for selecting optimal cut-points are only applicable to diseases with binary or three stages. Currently, there exist two diagnostic measures for diseases with general k stages: the hypervolume under the manifold and the generalized Youden index. While hypervolume under the manifold cannot be used for cut-points selection, generalized Youden index is only defined upon correct classification rates. This paper proposes a new measure named maximum absolute determinant for diseases with k stages ([Formula: see text]). This comprehensive new measure utilizes all the available classification information and serves as a cut-points selection criterion as well. Both the geometric and probabilistic interpretations for the new measure are examined. Power and simulation studies are carried out to investigate its performance as a measure of diagnostic accuracy as well as cut-points selection criterion. A real data set from Alzheimer's Disease Neuroimaging Initiative is analyzed using the proposed maximum absolute determinant.

Construction of joint confidence regions for the optimal true class fractions of Receiver Operating Characteristic (ROC) surfaces and manifolds

The three-class approach is used for progressive disorders when clinicians and researchers want to diagnose or classify subjects as members of one of three ordered categories based on a continuous diagnostic marker. The decision thresholds or optimal cut-off points required for this classification are often chosen to maximize the generalized Youden index (Nakas et al., Stat Med 2013; 32: 995-1003). The effectiveness of these chosen cut-off points can be evaluated by estimating their corresponding true class fractions and their associated confidence regions. Recently, in the two-class case, parametric and non-parametric methods were investigated for the construction of confidence regions for the pair of the Youden-index-based optimal sensitivity and specificity fractions that can take into account the correlation introduced between sensitivity and specificity when the optimal cut-off point is estimated from the data (Bantis et al., Biomet 2014; 70: 212-223). A parametric approach based on the Box-Cox transformation to normality often works well while for markers having more complex distributions a non-parametric procedure using logspline density estimation can be used instead. The true class fractions that correspond to the optimal cut-off points estimated by the generalized Youden index are correlated similarly to the two-class case. In this article, we generalize these methods to the three- and to the general k-class case which involves the classification of subjects into three or more ordered categories, where ROC surface or ROC manifold methodology, respectively, is typically employed for the evaluation of the discriminatory capacity of a diagnostic marker. We obtain three- and multi-dimensional joint confidence regions for the optimal true class fractions. We illustrate this with an application to the Trail Making Test Part A that has been used to characterize cognitive impairment in patients with Parkinson's disease.

Better see to better agree: phosphohistone H3 increases interobserver agreement in mitotic count for meningioma grading and imposes new specific thresholds

BACKGROUND

Mitotic count on hematoxylin and eosin (H&E)-stained slides is a crucial diagnostic criterion in meningioma grading. However, mitosis assessment on H&E slides can be impaired by technical factors and by pathologist's experience. Phosphohistone H3 (PHH3) serine-10 is a mitosis-specific antibody that has proven to facilitate mitotic count in various tumors.

METHODS

A series of 70 meningiomas (15 grade I, 40 grade II, 15 grade III) was used to validate PHH3 intra- and interobserver reproducibility and to identify PHH3-specific mitotic thresholds. Four pathologists with different experience in neuropathology counted mitoses on both H&E- and PHH3-stained slides.

RESULTS

H&E and PHH3 mitotic rates were highly correlated (Pearson's r = 0.92, P < .0001). PHH3 mitotic counts had both a good mean interobserver correlation (R(m) = 0.83) and a good intraclass correlation (0.78), higher than H&E mitotic indices (R(m) = 0.77, intraclass correlation = 0.71). After further stratification of meningiomas according to World Health Organization grade, PHH3 performed better in terms of interobserver concordance (Kendall's W = 0.761) compared with H&E (Kendall's W = 0.697). Referring to the same meningioma groups identified by World Health Organization grade as the gold standard, the volume under the receiver operator characteristic surface was 0.91, indicating a very good diagnostic ability of PHH3 scores in discriminating the 3 meningioma groups. The 2 optimal PHH3-specific cutoff values were 6.61 and 22.02.

CONCLUSION

PHH3 staining is a useful diagnostic complementary tool to standard H&E mitotic count, optimizing intra- and interobserver reproducibility. PHH3-specific mitotic thresholds should be adopted to avoid overgrading of meningioma when ancillary methods are employed.

CSF Biomarkers of Alzheimer’s Disease: Impact on Disease Concept, Diagnosis, and Clinical Trial Design

Data from clinicopathologic and biomarker studies have converged to support the view of Alzheimer’s disease (AD) as a continuum, with pathology developing decades prior to the onset of cognitive symptoms which culminate as dementia at the end stage of the disease. This concept is impacting disease nomenclature, diagnostic criteria, prognostic potential, and clinical trial design. Revisions to diagnostic criteria to incorporate biomarker results have recently been proposed in order to increase the confidence of AD as the underlying etiology of a clinical impairment and to permit a diagnosis of AD across the disease continuum, eventually perhaps in the asymptomatic period. Individuals in this preclinical stage are receiving intense focus as a targeted population for secondary prevention trials aimed at identifying disease-modifying therapies that have the best chance of preserving normal cognitive function. The goal is to bring validated biomarkers to clinical practice for the purpose of disease diagnosis, prognosis, and evaluation of therapeutic efficacy once disease-modifying treatments become available. Realization of this goal requires worldwide biomarker standardization efforts, consensus among researchers and clinicians regarding the clinical utility of assessing biomarkers in patient care settings, and eventually the endorsement and adoption of such procedures and practices into global health care systems.

Progress update: fluid and imaging biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a growing health crisis around the world. Although significant progress has been made in our understanding of AD pathogenesis, there is currently no effective treatment to delay onset or prevent the disease. The focus has now shifted to the identification and treatment of AD in the early clinical stages as well as before cognitive symptoms emerge-during the long preclinical stage. It is possible that diagnosis of individuals with AD will be more accurate when clinical symptoms and signs are combined with biomarkers, which can improve both the diagnostic and prognostic accuracy of AD and its differentiation from the other neurodegenerative diseases. This review discusses fluid and imaging biomarkers that have shown promise in such areas, as well as some of the current challenges that face the field.

DiagTest3Grp: An R Package for Analyzing Diagnostic Tests with Three Ordinal Groups

Medical researchers endeavor to identify potentially useful biomarkers to develop marker-based screening assays for disease diagnosis and prevention. Useful summary measures which properly evaluate the discriminative ability of diagnostic markers are critical for this purpose. Literature and existing software, for example, R packages nicely cover summary measures for diagnostic markers used for the binary case (e.g., healthy vs. diseased). An intermediate population at an early disease stage usually exists between the healthy and the fully diseased population in many disease processes. Supporting utilities for three-group diagnostic tests are highly desired and important for identifying patients at the early disease stage for timely treatments. However, application packages which provide summary measures for three ordinal groups are currently lacking. This paper focuses on two summary measures of diagnostic accuracy-volume under the receiver operating characteristic surface and the extended Youden index, with three diagnostic groups. We provide the R package DiagTest3Grp to estimate, under both parametric and nonparametric assumptions, the two summary measures and the associated variances, as well as the optimal cut-points for disease diagnosis. An omnibus test for multiple markers and a Wald test for two markers, on independent or paired samples, are incorporated to compare diagnostic accuracy across biomarkers. Sample size calculation under the normality assumption can be performed in the R package to design future diagnostic studies. A real world application evaluating the diagnostic accuracy of neuropsychological markers for Alzheimer's disease is used to guide readers through step-by-step implementation of DiagTest3Grp to demonstrate its utility.