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BackgroundTreatment with phosphodiesterase type 5 inhibitors (PDE-5is) is effective in treating erectile dysfunction (ED).AimThe objective of this study was to determine the effect of PDE-5is on the incidence of major adverse cardiovascular (CV) events (MACE; composite outcome of CV death, hospitalization for myocardial infarction, coronary revascularization, stroke, heart failure, and unstable angina pectoris) and overall mortality.MethodsA retrospective observational cohort study was conducted in a large US claims database in men with ≥1 diagnosis of ED without prior MACE within 1 year, from January 1, 2006, to October 31, 2020. The exposed group had ≥1 claim for PDE-5i and the unexposed group had no claims for PDE-5i, and the groups were matched up to 1:4 on baseline risk variables.OutcomeThe primary outcome was MACE and the secondary outcomes were overall mortality and individual components of MACE, determined by multivariable Cox proportional hazard modeling.ResultsMatched plus multivariable analyses showed that MACE was lower by 13% in men exposed (n = 23 816) to PDE-5is (hazard ratio [HR] 0.87; 95% CI 0.79-0.95; P = .001) vs nonexposure (n = 48 682) over mean follow-up periods of 37 and 29 months, respectively, with lower incidence of coronary revascularization (HR 0.85; 95% CI 0.73-0.98; P = .029), heart failure (HR 0.83; 95% CI 0.72-0.97; P = .016), unstable angina (HR 0.78; 95% CI 0.64-0.96; P = .021), and CV death (HR 0.61; 95% CI 0.41-0.90; P = .014) with PDE-5i exposure. Phosphodiesterase type 5 inhibitor–exposed men had a 25% lower incidence of overall mortality (HR 0.75; 95% CI 0.65-0.87; P Clinical ImplicationsPDE-5is may have cardioprotective effects.Strengths and LimitationsStrengths are the large numbers of participants and consistency of the data; limitations include the retrospective nature of the study and unknown confounders.ConclusionsIn a large population of US men with ED, PDE-5i exposure was associated with lower incidence of MACE, CV death, and overall mortality risk compared to non-exposure. Risk reduction correlated with PDE-5i exposure level.

With the advent of human-induced pluripotent stem cells (hiPSCs) differentiation protocols, different methods to createin-vitrohuman-derived neuronal networks have been proposed. Although monolayer cultures represent a valid model, adding three-dimensionality would make them more representative of anin-vivoenvironment. Thus, human-derived neurospheroids and brain-organoids are becoming increasingly used forin-vitrodisease modeling. Achieving control over the final cell composition and investigating the exhibited electrophysiological activity is still a challenge. Thence, platforms capable of measuring and characterizing the functional aspects of these samples are needed. Here, we propose a method to rapid generate neurospheroids of human origin with control over cell composition that can be used for functional investigations. We show a characterization of the electrophysiological activity exhibited by the neurospheroids by presenting for the first-time results from the main micro-electrodes arrays (MEAs) types available on the market (passive electrode, C-MOS electrodes, 3D electrodes). Neurospheroids grown in free culture and transferred on MEA exhibited functional activity that can be chemically and electrically modulated. Our results indicates that this model holds great potential for in-depth study of signal transmission to drug screening, disease modeling and offers a reproducible and stable platform forin-vitrofunctional testing.

Supplementary Figure S2. ROC curves for two biomarkers, A (red) and B (blue).

Supplemental Figure and Table Legend

Background: Biomarker discovery research has yielded few biomarkers that validate for clinical use. A contributing factor may be poor study designs.Methods: The goal in discovery research is to identify a subset of potentially useful markers from a large set of candidates assayed on case and control samples. We recommend the PRoBE design for selecting samples. We propose sample size calculations that require specifying: (i) a definition for biomarker performance; (ii) the proportion of useful markers the study should identify (Discovery Power); and (iii) the tolerable number of useless markers amongst those identified (False Leads Expected, FLE).Results: We apply the methodology to a study of 9,000 candidate biomarkers for risk of colon cancer recurrence where a useful biomarker has positive predictive value ≥ 30%. We find that 40 patients with recurrence and 160 without recurrence suffice to filter out 98% of useless markers (2% FLE) while identifying 95% of useful biomarkers (95% Discovery Power). Alternative methods for sample size calculation required more assumptions.Conclusions: Biomarker discovery research should utilize quality biospecimen repositories and include sample sizes that enable markers meeting prespecified performance characteristics for well-defined clinical applications to be identified.Impact: The scientific rigor of discovery research should be improved. Cancer Epidemiol Biomarkers Prev; 24(6); 944–50. ©2015 AACR.

Supplementary Figures 1-2, Tables 1-5. Supplemental Figure 1. Flow diagrams showing the design and collection of the reference set. Supplemental Figure 2. Clustering of the 90 analytes across all 505 subjects. Supplemental Table 1: Subjects in the final breast cancer reference set according to diagnosis and site of collection. Supplemental Table 2: The identity of the biomarkers analyzed and their membership in specific multiplexed panels. Supplemental Table 3: Subject demographics in the Breast Cancer Reference Sets. Supplemental Table 4: Significant differences in marker levels between subjects with benign breast disease without atypia compared to mammographic screen normal. Supplemental Table 5: The effect of co-variates on biomarker levels.

Participant and Clinical Information Form. Study forms used to obtain subject and clinical information.

Background: Advances in biotechnology have raised expectations that biomarkers, including genetic profiles, will yield information to accurately predict outcomes for individuals. However, results to date have been disappointing. In addition, statistical methods to quantify the predictive information in markers have not been standardized.Methods: We discuss statistical techniques to summarize predictive information, including risk distribution curves and measures derived from them, that relate to decision making. Attributes of these measures are contrasted with alternatives such as receiver operating characteristic curves, R2, percent reclassification, and net reclassification index. Data are generated from simple models of risk conferred by genetic profiles for individuals in a population. Statistical techniques are illustrated, and the risk prediction capacities of different risk models are quantified.Results: Risk distribution curves are most informative and relevant to clinical practice. They show proportions of subjects classified into clinically relevant risk categories. In a population in which 10% have the outcome event and subjects are categorized as high risk if their risk exceeds 20%, we identified some settings where more than half of those destined to have an event were classified as high risk by the risk model. Either 150 genes each with odds ratio of 1.5 or 250 genes each with odds ratio of 1.25 were required when the minor allele frequencies are 10%. We show that conclusions based on receiver operating characteristic curves may not be the same as conclusions based on risk distribution curves.Conclusions: Many highly predictive genes will be required to identify substantial numbers of subjects at high risk. Cancer Epidemiol Biomarkers Prev; 19(3); 655–65

Supplementary Table S1. Illustrative data from a discovery study

Supplementary Appendix from The Potential of Genes and Other Markers to Inform about Risk

Background: Many circulating biomarkers have been reported for the diagnosis of breast cancer, but few, if any, have undergone rigorous credentialing using prospective cohorts and blinded evaluation.Methods: The NCI Early Detection Research Network (EDRN) has created a prospective, multicenter collection of plasma and serum samples from 832 subjects designed to evaluate circulating biomarkers for the detection and diagnosis of breast cancer. These samples are available to investigators who wish to evaluate their biomarkers using a set of blinded samples. The breast cancer reference set is composed of blood samples collected using a standard operating procedure at four U.S. medical centers from 2008 to 2010 from women undergoing either tissue diagnosis for breast cancer or routine screening mammography. The reference set contains samples from women with incident invasive cancer (n = 190), carcinoma in situ (n = 55), benign pathology with atypia (n = 63), benign disease with no atypia (n = 231), and women with no evidence of breast disease by screening mammography (BI-RADS 1 or 2, n = 276). Using a subset of plasma samples (n = 505) from the reference set, we analyzed 90 proteins by multiplexed immunoassays for their potential utility as diagnostic markers.Results: We found that none of these markers is useful for distinguishing cancer from benign controls. However, elevated CA-125 does appear to be a candidate marker for estrogen receptor–negative cancers.Conclusions: Markers that can distinguish benign breast conditions from invasive cancer have not yet been found.Impact: Availability of prospectively collected samples should improve future validation efforts. Cancer Epidemiol Biomarkers Prev; 24(2); 435–41. ©2014 AACR.

Supplementary Materials and Methods

Objective: The association of T2D and severe C-19 outcomes has been reported, but data are scarce on whether pre-existing CVD impacts C-19 outcomes in T2D patients. This study compared the clinical outcomes among C-19 patients with pre-existing T2D, T2D+CVD, or neither. Methods: Retrospective study of claims and lab data to identify C-19 patients from 3/1/20 - 5/31/21 and stratify them by the presence of T2D and CVD. Outcomes included hospital/ICU admission and mortality. Propensity score matching and multivariable analyses were performed. Results: After matching, 6,967 patients were identified for each group with a mean follow-up of 5.4 months; minimal residual baseline differences remained. Adjusted analyses (Table) show that C-19 patients with T2D+CVD or T2D only were more likely to be admitted to the hospital/ICU than those with neither (all p Conclusion: Our study highlights the incrementally poorer outcomes associated with pre-existing T2D+CVD in C-19 patients and may suggest consideration of a more aggressive management approach in these patients with a less favorable prognosis. Disclosure C. Nguyen: Employee; HealthCore Inc. T. P. Power: None. R. Mehra: None. V. Willey: Employee; HealthCore Inc. C. Crowe: None. E. Kuti: Employee; Boehringer Ingelheim International GmbH. N. Narsipur: Employee; Boehringer Ingelheim International GmbH, Janssen Scientific Affairs, LLC. B. M. K. Donato: Employee; Boehringer Ingelheim International GmbH. R. S. Pepe: None. R. L. Djaraher: None. L. J. Seman: n/a. N. Graeter: Employee; Boehringer Ingelheim International GmbH. Funding Boehringer Ingelheim Pharmaceuticals Inc.

Background: Phosphodiesterase type 5 inhibitor (PDE-5i) medications are effective in treating Erectile Dysfunction (ED) for tens of millions of men in the US and worldwide. The objective of this study was to determine the effect of PDE-5is on the incidence of major adverse cardiovascular events (MACE) (composite outcome of cardiovascular (CV) death, hospitalization for myocardial infarction, coronary revascularization, stroke, heart failure, or unstable angina pectoris) and all-cause death. Methods: A retrospective observational cohort study was conducted in a large US commercial and Medicare insurance claims database in men with ≥1 diagnosis of ED without prior MACE hospitalization within 1 year from Jan 2006 to Oct 2020. The exposed group had ≥1 claim for PDE-5i; the unexposed group had no claims for PDE-5i and were matched 2:1 on baseline risk variables. The primary outcome was MACE and secondary outcome was all-cause death, determined by multivariate Cox proportional hazard modeling. Death data were obtained via National Death Index linkage. Results: In this population, MACE was reduced by 13% in men exposed (n=23,816) to PDE-5is (hazard ratio (HR) 0.87; 95% Confidence Interval (CI) 0.79-0.95) vs. non-exposure (n=48,682) over mean follow up of 29 and 37 months, respectively. This was driven by reductions in coronary revascularization (HR 0.85; 0.73-0.98), heart failure (HR 0.83; 0.72-0.97), unstable angina (HR 0.78; 0.64-0.96), and CV death (HR 0.61; 0.41-0.90). PDE-5i-exposed men had a 25% reduction in all-cause death (HR 0.75; 0.65-0.87). Men without coronary artery disease but with CV risk factors at baseline showed a similar pattern. In the main study cohort, men in the highest quartile of PDE-5i exposure had the greatest reductions in MACE (HR 0.45; 0.37-0.54) and all-cause death (HR 0.51; 0.37-0.71) vs. the lowest exposure quartile. In a subgroup with baseline type 2 diabetes (n=6,305), PDE-5i exposure was associated with a lower MACE risk (HR 0.79; 0.64-0.97). All comparisons are significant at p Conclusions: In a large population of US men with ED, PDE-5i exposure was associated with reductions in MACE, CV death, and overall death risk compared to non-exposure. Risk reduction was positively correlated with PDE-5i exposure level.

The cancer early-detection biomarker field was, compared with the therapeutic arena, in its infancy when the Early Detection Research Network (EDRN) was initiated in 2000. The EDRN has played a crucial role in changing the culture and the ways people conduct biomarker studies. The EDRN proposed biomarker developmental guidelines and biomarker pivotal trial study design standards, created biomarker reference sets and functioned as an unbiased broker for the field, implemented the most rigorous blinding policy in the biomarker field, developed an array of statistical and computational tools for early-detection biomarker evaluations, and developed a multidisciplinary team-science approach. We reviewed these contributions made by the EDRN and their impacts on maturing the field. Future challenges and opportunities in cancer early-detection biomarker translational research are discussed, particularly in strengthening biomarker discovery pipeline and conducting more efficient biomarker validation studies. See all articles in this CEBP Focus section, “NCI Early Detection Research Network: Making Cancer Detection Possible.”

Previous studies of second opinions in the diagnosis of melanocytic skin lesions have examined blinded second opinions, which do not reflect usual clinical practice. The current study, conducted in the USA, investigated both blinded and nonblinded second opinions for their impact on diagnostic accuracy.In total, 100 melanocytic skin biopsy cases, ranging from benign to invasive melanoma, were interpreted by 74 dermatopathologists. Subsequently, 151 dermatopathologists performed nonblinded second and third reviews. We compared the accuracy of single reviewers, second opinions obtained from independent, blinded reviewers and second opinions obtained from sequential, nonblinded reviewers. Accuracy was defined with respect to a consensus reference diagnosis.The mean case-level diagnostic accuracy of single reviewers was 65.3% (95% CI 63.4-67.2%). Second opinions arising from sequential, nonblinded reviewers significantly improved accuracy to 69.9% (95% CI 68.0-71.7%; P 0.001). Similarly, second opinions arising from blinded reviewers improved upon the accuracy of single reviewers (69.2%; 95% CI 68.0-71.7%). Nonblinded reviewers were more likely than blinded reviewers to give diagnoses in the same diagnostic classes as the first diagnosis. Nonblinded reviewers tended to be more confident when they agreed with previous reviewers, even with inaccurate diagnoses.We found that both blinded and nonblinded second reviewers offered a similar modest improvement in diagnostic accuracy compared with single reviewers. Obtaining second opinions with knowledge of previous reviews tends to generate agreement among reviews, and may generate unwarranted confidence in an inaccurate diagnosis. Combining aspects of both blinded and nonblinded review in practice may leverage the advantages while mitigating the disadvantages of each approach. Specifically, a second pathologist could give an initial diagnosis blinded to the results of the first pathologist, with subsequent nonblinded discussion between the two pathologists if their diagnoses differ.

Treatment for pulmonary arterial hypertension (PAH) has evolved over the past decade, including approval of new medications and growing evidence to support earlier use of combination therapy. Despite these changes, few studies have assessed real-world treatment patterns, healthcare resource utilization (HCRU), and costs among people with PAH using recent data. We conducted a retrospective cohort study using administrative claims from the HealthCore Integrated Research Database®. Adult members with claims for a PAH diagnosis, right heart catheterization, and who initiated PAH treatment (index date) between October 1, 2015 and November 30, 2020 were identified. Members had to be continuously enrolled in the health plan for 6 months before the index date (baseline) and ≥30 days after. Treatment patterns, HCRU, and costs were described. A total of 843 members with PAH (mean age 62.3 years, 64.2% female) were included. Only 21.0% of members received combination therapy as their first-line treatment, while most members (54.6%) received combination therapy as second-line treatment. All-cause HCRU remained high after treatment initiation with 58.0% of members having ≥1 hospitalization and 41.3% with ≥1 emergency room visit. Total all-cause costs declined from $15,117 per patient per month at baseline to $14,201 after treatment initiation, with decreased medical costs ($14,208 vs. $6,349) more than offsetting increased pharmacy costs ($909 vs. $7,852). In summary, despite growing evidence supporting combination therapy, most members with PAH initiated treatment with monotherapy. Total costs decreased following treatment, driven by a reduction in medical costs even with increases in pharmacy costs.

Biomarkers abound in many areas of clinical research, and often investigators are interested in combining them for diagnosis, prognosis, or screening. In many applications, the true positive rate for a biomarker combination at a prespecified, clinically acceptable false positive rate is the most relevant measure of predictive capacity. We propose a distribution-free method for constructing biomarker combinations by maximizing the true positive rate while constraining the false positive rate. Theoretical results demonstrate desirable properties of biomarker combinations produced by the new method. In simulations, the biomarker combination provided by our method demonstrated improved operating characteristics in a variety of scenarios when compared with alternative methods for constructing biomarker combinations., 37 pages (including appendices)

RESEARCH OBJECTIVE: Prescribing opioids, particularly the number of pills, is associated with greater likelihood of patients developing longer‐term opioid dependence. Nudges targeted to clinicians are a low‐cost strategy that could reduce unnecessary opioid prescribing. In particular, clinician‐focused peer comparison feedback has been effective in influencing prescribing for other medications. However, peer comparison feedback has not been well‐tested for opioid prescribing, alone or compared against other feedback approaches such as individual audit feedback. STUDY DESIGN: We conducted a pragmatic, four‐arm factorial cluster randomized clinical trial among 48 emergency department (ED) and urgent care (UC) practice sites within Sutter Health System including a 6‐month pre‐intervention period and a 6‐month intervention. 438 clinicians were cluster randomized by practice site. Interventions were delivered to clinicians monthly by email. Peer comparison feedback included the number of pills per opioid prescription and proportion of encounters with an opioid prescription in the prior 3 months, relative to that of their practice site peers. Individual audit feedback included the number of prescriptions with >30 pills in the prior 3 months, indicating potential outlier prescriptions. The primary outcome measure was the change in the number of pills per prescription. Secondary outcomes included changes in the proportion of pills and milligrams of morphine equivalents (MME) per prescription. POPULATION STUDIED: The sample included 263 ED and 175 UC clinicians comprised of 294,962 patient encounters, with a mean (SD) age of 49 years (19), 56% female, 9% Black, and 21% Hispanic. PRINCIPAL FINDINGS: During the pre‐intervention period, there was a mean (SD) of 15.1 pills (3.9) and 76.3 MME (23.0) per prescription, and 9.3% of encounters with an opioid prescription. In adjusted analyses compared to usual care during the intervention, there was a significant decrease in pills per prescription among clinicians receiving peer comparison feedback alone (−0.9 pills per prescription; 95% CI ‐1.5 to −0.3, P = 0.002) and receiving both peer comparison and individual audit feedback (−1.4 pills per prescription; 95% CI ‐2 to −0.8, P

Importance Statin therapy is underused for many patients who could benefit. Objective To evaluate the effect of passive choice and active choice interventions in the electronic health record (EHR) to promote guideline-directed statin therapy. Design, setting, and participants Three-arm randomized clinical trial with a 6-month preintervention period and 6-month intervention. Randomization conducted at the cardiologist level at 16 cardiology practices in Pennsylvania and New Jersey. The study included 82 cardiologists and 11 693 patients. Data were analyzed between May 8, 2019, and January 9, 2020. Interventions In passive choice, cardiologists had to manually access an alert embedded in the EHR to select options to initiate or increase statin therapy. In active choice, an interruptive EHR alert prompted the cardiologist to accept or decline guideline-directed statin therapy. Cardiologists in the control group were informed of the trial but received no other interventions. Main outcomes and measures Primary outcome was statin therapy at optimal dose based on clinical guidelines. Secondary outcome was statin therapy at any dose. Results The sample comprised 11 693 patients with a mean (SD) age of 63.8 (9.1) years; 58% were male (n = 6749 of 11 693), 66% were White (n = 7683 of 11 693), and 24% were Black (n = 2824 of 11 693). The mean (SD) 10-year atherosclerotic cardiovascular disease (ASCVD) risk score was 15.4 (10.0); 68% had an ASVCD clinical diagnosis. Baseline statin prescribing rates at the optimal dose were 40.3% in the control arm, 39.1% in the passive choice arm, and 41.2% in the active choice arm. In adjusted analyses, the change in statin prescribing rates at optimal dose over time was not significantly different from control for passive choice (adjusted difference in percentage points, 0.2; 95% CI, -2.9 to 2.8; P = .86) or active choice (adjusted difference in percentage points, 2.4; 95% CI, -0.6 to 5.0; P = .08). In adjusted analyses of the subset of patients with clinical ASCVD, the active choice intervention resulted in a significant increase in statin prescribing at optimal dose relative to control (adjusted difference in percentage points, 3.8; 95% CI, 1.0-6.4; P = .008). No other subset analyses were significant. There were no significant changes in statin prescribing at any dose for either intervention. Conclusions and relevance The passive choice and active choice interventions did not change statin prescribing. In the subgroup of patients with clinical ASCVD, the active choice intervention led to a small increase in statin prescribing at the optimal dose, which could inform the design or targeting of future interventions. Trial registration ClinicalTrials.gov Identifier: NCT03271931.

Background Biomarker candidates are often ranked using P-values. Standard P-value calculations use normal or logit-normal approximations, which may not be correct for small P-values and small sample sizes common in discovery research. Methods We compared exact P-values, correct by definition, with logit-normal approximations in a simulated study of 40 cases and 160 controls. The key measure of biomarker performance was sensitivity at 90% specificity. Data for 3000 uninformative false markers and 30 informative true markers were generated randomly. We also analyzed real data for 2371 plasma protein markers measured in 121 breast cancer cases and 121 controls. Results In our simulation, using the same discovery criterion, exact P-values led to discovery of 24 true and 82 false biomarkers, while logit-normal approximate P-values yielded 20 true and 106 false biomarkers. The estimated true discovery rate was substantially off for approximate P-values: logit-normal estimated 42 but found 20. The exact method estimated 22, very close to 24, which was the actual number of true discoveries. Although these results are based on one specific simulation, qualitatively similar results were obtained from 10 random repetitions. With real data, ranking candidate biomarkers by exact P-values, versus approximate P-values, resulted in a very different ordering of these markers. Conclusions Exact P-values, which correspond to permutation tests with non-parametric rank statistics such as empirical ROC statistics, are preferred over approximate P-values. Approximate P-values can lead to inappropriate biomarker selection rules and incorrect conclusions. Impact Exact P-values in place of approximate P-values in discovery research may improve the yield of biomarkers that validate clinically.

Importance Diagnostic variation among pathologists interpreting cutaneous melanocytic lesions could lead to suboptimal care. Objective To estimate the potential association of second-opinion strategies in the histopathologic diagnosis of cutaneous melanocytic lesions with diagnostic accuracy and 1-year population-level costs in the US. Design, Setting, and Participants Decision analysis with 1-year time horizon including melanocytic lesion diagnoses available from US pathologists participating in the Melanoma Pathology Study (M-Path) and from the study panel of reference pathologists who classified cases using the MPATH-Dx classification tool. M-Path data collection occurred from July 2013 through March 2015; analyses for the present study were performed between April 2015 and January 2021. Exposures Various second-opinion strategies for interpretation of melanocytic cutaneous lesions. Main Outcomes and Measures Estimated accuracy of pathologists’ diagnoses, defined as concordance with the reference panel diagnoses, and 1-year postbiopsy medical costs under various second-opinion strategies. Expected percentage of concordant diagnoses, including percentages of overinterpretation and underinterpretation, and 1-year costs of medical care per 100 000 in the US population. Results Decision-analytic model parameters were based on diagnostic interpretations for 240 cases by 187 pathologists compared with reference panel diagnoses. Without second opinions, 83.2% of diagnoses in the US were estimated to be accurate—ie, concordant with the reference diagnosis; with overinterpretation (8.0%) or underinterpretation (8.8%), and 16 850 misclassified diagnoses per 100 000 biopsies. Accuracy increased under all second-opinion strategies. Accuracy (87.4% concordance with 3.6% overinterpretation and 9.1% underinterpretation) and cost (an increase of more than $10 million per 100 000 biopsies per year) were highest when second opinions were universal (eg, performed on all biopsies), relative to no second opinions. A selective second-opinion strategy based on pathologists’ desire or institutional requirements for a second opinion was most accurate (86.5% concordance; 4.4% overinterpretation; 9.1% underinterpretation) and would reduce costs by more than $1.9 million per 100 000 skin biopsies relative to no second opinions. Improvements in diagnostic accuracy with all second-opinion strategies were associated with reductions in overinterpretation but not underinterpretation. Conclusions and Relevance In this decision-analytic model, selective second-opinion strategies for interpretation of melanocytic skin lesions showed the potential to improve diagnostic accuracy and decrease costs relative to no second opinions or universal second opinions.

Referral strategies based on risk scores and medical tests are commonly proposed. Direct assessment of their clinical utility requires implementing the strategy and is not possible in the early phases of biomarker research. Prior to late-phase studies, net benefit measures can be used to assess the potential clinical impact of a proposed strategy. Validation studies, in which the biomarker defines a prespecified referral strategy, are a gold standard approach to evaluating biomarker potential. Uncertainty, quantified by a confidence interval, is important to consider when deciding whether a biomarker warrants an impact study, does not demonstrate clinical potential, or that more data are needed. We establish distribution theory for empirical estimators of net benefit and propose empirical estimators of variance. The primary results are for the most commonly employed estimators of net benefit: from cohort and unmatched case-control samples, and for point estimates and net benefit curves. Novel estimators of net benefit under stratified two-phase and categorically matched case-control sampling are proposed and distribution theory developed. Results for common variants of net benefit and for estimation from right-censored outcomes are also presented. We motivate and demonstrate the methodology with examples from lung cancer research and highlight its application to study design.

Key Points Question Can different forms of financial incentives (unconditional, conditional, or lottery) boost response rates to mailed colorectal cancer screening outreach? Findings In this randomized clinical trial of 897 patients, there was no statistically significant difference in screening response rates at 2 and 6 months between the incentive arms and mailed outreach without incentive. Meaning Different forms of financial incentives of the same expected value ($10) did not increase fecal immunochemical test completion rates, as the incentive value have been too small or financial incentives may not be effective in this context., Importance Mailing fecal immunochemical test (FIT) kits to patients’ homes has been shown to boost colorectal cancer screening rates, but response rates remain limited, and organized programs typically require repeated outreach attempts. Behavioral economics has shown that offering salient financial incentives to patients may increase participation in preventive health. Objective To compare the impact of different financial incentives for mailed FIT outreach. Design, Setting, and Participants This 4-parallel-arm randomized clinical trial included patients aged 50 to 75 years who had an established primary care clinician, at least 2 visits in the prior 2 years, and were eligible for colorectal cancer screening and not up-to-date. This study was conducted at urban primary care practices at an academic health system from December 2015 to February 2018. Data analysis was conducted from March 2018 to September 2018. Interventions Eligible patients received a letter from their primary care clinician that included a mailed FIT kit and instructions for use. They were randomized in a 1:1:1:1 ratio to receive (1) no financial incentive; (2) an unconditional $10 incentive included with the mailing; (3) a $10 incentive conditional on FIT completion; or (4) a conditional lottery with a 1-in-10 chance of winning $100 after FIT completion. Main Outcomes and Measures Fecal immunochemical test completion within 2 and 6 months of initial outreach. Results A total of 897 participants were randomized, with a median age of 57 years (interquartile range, 52-62 years); 56% were women, and 69% were black. The overall completion rate across all arms was 23.5% at 2 months. The completion rate at 2 months was 26.0% (95% CI, 20.4%-32.3%) in the no incentive arm, 27.2% (95% CI, 21.5%-33.6%) in the unconditional incentive arm, 23.2% (95% CI, 17.9%-29.3%) in the conditional incentive arm, and 17.7% (95% CI, 13.0%-23.3%) in the lottery incentive arm. None of the arms with an incentive were statistically superior to the arm without incentive. The overall FIT completion rate across all arms was 28.9% at 6 months, and there was also no difference by arm. The completion rate at 6 months was 32.7% (95% CI, 26.6%-39.3%) in the no incentive arm, 31.7% (95% CI, 25.7%-38.2%) in the unconditional incentive arm, 26.8% (95% CI, 21.1%-33.1%) in the conditional incentive arm, and 24.3% (95% CI, 18.9%-30.5%) in the lottery incentive arm. Conclusions and Relevance Mailed FIT resulted in high colorectal cancer screening response rates in this population, but different forms of financial incentives of the same expected value ($10) did not incrementally increase FIT completion rates. The incentive value may have been too small or financial incentives may not be effective in this context. Trial Registration ClinicalTrials.gov Identifier: NCT02594150, This 4-parallel-arm randomized clinical trial determines the effectiveness of financial incentives on fecal immunochemical test screening completion vs no incentive among adults who were sent kits by mail.

Surgeons may receive a different diagnosis when a breast biopsy is interpreted by a second pathologist. The extent to which diagnostic agreement by the same pathologist varies at two time points is unknown. Pathologists from eight U.S. states independently interpreted 60 breast specimens, one glass slide per case, on two occasions separated by ≥9 months. Reproducibility was assessed by comparing interpretations between the two time points; associations between reproducibility (intraobserver agreement rates); and characteristics of pathologists and cases were determined and also compared with interobserver agreement of baseline interpretations. Sixty-five percent of invited, responding pathologists were eligible and consented; 49 interpreted glass slides in both study phases, resulting in 2940 interpretations. Intraobserver agreement rates between the two phases were 92% [95% confidence interval (CI) 88–95] for invasive breast cancer, 84% (95% CI 81–87) for ductal carcinoma-in-situ, 53% (95% CI 47–59) for atypia, and 84% (95% CI 81–86) for benign without atypia. When comparing all study participants’ case interpretations at baseline, interobserver agreement rates were 89% (95% CI 84–92) for invasive cancer, 79% (95% CI 76–81) for ductal carcinoma-in-situ, 43% (95% CI 41–45) for atypia, and 77% (95% CI 74–79) for benign without atypia. Interpretive agreement between two time points by the same individual pathologist was low for atypia and was similar to observed rates of agreement for atypia between different pathologists. Physicians and patients should be aware of the diagnostic challenges associated with a breast biopsy diagnosis of atypia when considering treatment and surveillance decisions.

The effect of heat-moisture treatment on structural, physicochemical, and rheological characteristics of arrowroot starch was investigated. Heat-moisture treatment was performed with starch samples conditioned to 28% moisture at 100℃ for 2, 4, 8, and 16 h. Structural and physicochemical characterization of native and modified starches, as well as rheological assays with gels of native and 4 h modified starches subjected to acid and sterilization stresses were performed. Arrowroot starch had 23.1% of amylose and a CA-type crystalline pattern that changed over the treatment time to A-type. Modified starches had higher pasting temperature and lower peak viscosity while breakdown viscosity practically disappeared, independently of the treatment time. Gelatinization temperature and crystallinity increased, while enthalpy, swelling power, and solubility decreased with the treatment. Gels from modified starches, independently of the stress conditions, were found to have more stable apparent viscosities and higher G′ and G″ than gels from native starch. Heat-moisture treatment caused a reorganization of starch chains that increased molecular interactions. This increase resulted in higher paste stability and strengthened gels that showed higher resistance to shearing and heat, even after acid or sterilization conditions. A treatment time of 4 h was enough to deeply changing the physicochemical properties of starch.

Background: Many circulating biomarkers have been reported for the diagnosis of breast cancer, but few, if any, have undergone rigorous credentialing using prospective cohorts and blinded evaluation. Methods: The NCI Early Detection Research Network (EDRN) has created a prospective, multicenter collection of plasma and serum samples from 832 subjects designed to evaluate circulating biomarkers for the detection and diagnosis of breast cancer. These samples are available to investigators who wish to evaluate their biomarkers using a set of blinded samples. The breast cancer reference set is composed of blood samples collected using a standard operating procedure at four U.S. medical centers from 2008 to 2010 from women undergoing either tissue diagnosis for breast cancer or routine screening mammography. The reference set contains samples from women with incident invasive cancer (n = 190), carcinoma in situ (n = 55), benign pathology with atypia (n = 63), benign disease with no atypia (n = 231), and women with no evidence of breast disease by screening mammography (BI-RADS 1 or 2, n = 276). Using a subset of plasma samples (n = 505) from the reference set, we analyzed 90 proteins by multiplexed immunoassays for their potential utility as diagnostic markers. Results: We found that none of these markers is useful for distinguishing cancer from benign controls. However, elevated CA-125 does appear to be a candidate marker for estrogen receptor–negative cancers. Conclusions: Markers that can distinguish benign breast conditions from invasive cancer have not yet been found. Impact: Availability of prospectively collected samples should improve future validation efforts. Cancer Epidemiol Biomarkers Prev; 24(2); 435–41. ©2014 AACR.

Objective To quantify the accuracy and reproducibility of pathologists’ diagnoses of melanocytic skin lesions. Design Observer accuracy and reproducibility study. Setting 10 US states. Participants Skin biopsy cases (n=240), grouped into sets of 36 or 48. Pathologists from 10 US states were randomized to independently interpret the same set on two occasions (phases 1 and 2), at least eight months apart. Main outcome measures Pathologists’ interpretations were condensed into five classes: I (eg, nevus or mild atypia); II (eg, moderate atypia); III (eg, severe atypia or melanoma in situ); IV (eg, pathologic stage T1a (pT1a) early invasive melanoma); and V (eg, ≥pT1b invasive melanoma). Reproducibility was assessed by intraobserver and interobserver concordance rates, and accuracy by concordance with three reference diagnoses. Results In phase 1, 187 pathologists completed 8976 independent case interpretations resulting in an average of 10 (SD 4) different diagnostic terms applied to each case. Among pathologists interpreting the same cases in both phases, when pathologists diagnosed a case as class I or class V during phase 1, they gave the same diagnosis in phase 2 for the majority of cases (class I 76.7%; class V 82.6%). However, the intraobserver reproducibility was lower for cases interpreted as class II (35.2%), class III (59.5%), and class IV (63.2%). Average interobserver concordance rates were lower, but with similar trends. Accuracy using a consensus diagnosis of experienced pathologists as reference varied by class: I, 92% (95% confidence interval 90% to 94%); II, 25% (22% to 28%); III, 40% (37% to 44%); IV, 43% (39% to 46%); and V, 72% (69% to 75%). It is estimated that at a population level, 82.8% (81.0% to 84.5%) of melanocytic skin biopsy diagnoses would have their diagnosis verified if reviewed by a consensus reference panel of experienced pathologists, with 8.0% (6.2% to 9.9%) of cases overinterpreted by the initial pathologist and 9.2% (8.8% to 9.6%) underinterpreted. Conclusion Diagnoses spanning moderately dysplastic nevi to early stage invasive melanoma were neither reproducible nor accurate in this large study of pathologists in the USA. Efforts to improve clinical practice should include using a standardized classification system, acknowledging uncertainty in pathology reports, and developing tools such as molecular markers to support pathologists’ visual assessments.

PURPOSE: To estimate the potential near-term population impact of alternative second opinion breast biopsy pathology interpretation strategies. METHODS: Decision analysis examining 12-month outcomes of breast biopsy for nine breast pathology interpretation strategies in the U.S. health system. Diagnoses of 115 practicing pathologists in the Breast Pathology Study were compared to reference-standard-consensus diagnoses with and without second opinions. Interpretation strategies were defined by whether a second opinion was sought universally or selectively (e.g., 2nd opinion if invasive). Main outcomes were the expected proportion of concordant breast biopsy diagnoses, the proportion involving over-or under-interpretation, and cost of care in U.S. dollars within one-year of biopsy. RESULTS: Without a second opinion, 92.2% of biopsies received a concordant diagnosis. Concordance rates increased under all second opinion strategies, and the rate was highest (95.1%) and under-treatment lowest (2.6%) when all biopsies had second opinions. However, overtreatment was lowest when second opinions were sought selectively for initial diagnoses of invasive cancer, DCIS, or atypia (1.8 vs. 4.7% with no 2nd opinions). This strategy also had the lowest projected 12-month care costs ($5.907 billion vs. $6.049 billion with no 2nd opinions). CONCLUSIONS: Second opinion strategies could lower overall care costs while reducing both over-and under-treatment. The most accurate cost-saving strategy required second opinions for initial diagnoses of invasive cancer, DCIS, or atypia.

This diagnostic study evaluates whether fellowship training and board certification of physicians in dermatopathology affect the diagnostic reliability of second-opinion strategies for difficult-to-diagnose cutaneous melanocytic lesions., Key Points Question For cutaneous melanocytic lesions that are difficult to diagnose, do second opinions rendered by pathologists who have board certification and/or fellowship training in dermatopathology improve overall reliability of diagnosis? Findings In this diagnostic study of 240 melanocytic lesions from the Melanoma Pathology Study data set, misclassification of melanocytic lesions was lowest when first, second, and third consulting reviewers were subspecialty-trained dermatopathologists and when all lesions were subject to second opinions; misclassification was highest when reviewers were all general pathologists lacking subspecialty training. Variability of in situ and thin invasive melanoma was relatively intractable to all examined strategies. Meaning The findings suggest that second opinions rendered by dermatopathologists improve overall reliability of diagnosis of melanocytic lesions but do not eliminate or substantially reduce misclassification., Importance Histopathologic criteria have limited diagnostic reliability for a range of cutaneous melanocytic lesions. Objective To evaluate the association of second-opinion strategies by general pathologists and dermatopathologists with the overall reliability of diagnosis of difficult melanocytic lesions. Design, Setting, and Participants This diagnostic study used samples from the Melanoma Pathology Study, which comprises 240 melanocytic lesion samples selected from a dermatopathology laboratory in Bellevue, Washington, and represents the full spectrum of lesions from common nevi to invasive melanoma. Five sets of 48 samples were evaluated independently by 187 US pathologists from July 15, 2013, through May 23, 2016. Data analysis was performed from April 2016 through November 2017. Main Outcomes and Measures Accuracy of diagnosis, defined as concordance with an expert consensus diagnosis of 3 experienced pathologists, was assessed after applying 10 different second-opinion strategies. Results Among the 187 US pathologists examining the 24 lesion samples, 113 were general pathologists (65 men [57.5%]; mean age at survey, 53.7 years [range, 33.0-79.0 years]) and 74 were dermatopathologists (49 men [66.2%]; mean age at survey, 46.4 years [range, 33.0-77.0 years]). Among the 8976 initial case interpretations, physicians desired second opinions for 3899 (43.4%), most often for interpretation of severely dysplastic nevi. The overall misclassification rate was highest when interpretations did not include second opinions and initial reviewers were all general pathologists lacking subspecialty training (52.8%; 95% CI, 51.3%-54.3%). When considering different second opinion strategies, the misclassification of melanocytic lesions was lowest when the first, second, and third consulting reviewers were subspecialty-trained dermatopathologists and when all lesions were subject to second opinions (36.7%; 95% CI, 33.1%-40.7%). When the second opinion strategies were compared with single interpretations without second opinions, the reductions in misclassification rates for some of the strategies were statistically significant, but none of the strategies eliminated diagnostic misclassification. Melanocytic lesions in the middle of the diagnostic spectrum had the highest misclassification rates (eg, moderately or severely dysplastic nevus, Spitz nevus, melanoma in situ, and pathologic stage [p]T1a invasive melanoma). Variability of in situ and thin invasive melanoma was relatively intractable to all examined strategies. Conclusions and Relevance The results of this study suggest that second opinions rendered by dermatopathologists improve reliability of melanocytic lesion diagnosis. However, discordance among pathologists remained high.

Net reclassification indices have recently become popular statistics for measuring the prediction increment of new biomarkers. We review the various types of net reclassification indices and their correct interpretations. We evaluate the advantages and disadvantages of quantifying the prediction increment with these indices. For pre-defined risk categories, we relate net reclassification indices to existing measures of the prediction increment. We also consider statistical methodology for constructing confidence intervals for net reclassification indices and evaluate the merits of hypothesis testing based on such indices. We recommend that investigators using net reclassification indices should report them separately for events (cases) and nonevents (controls). When there are two risk categories, the components of net reclassification indices are the same as the changes in the true-positive and false-positive rates. We advocate use of true- and false-positive rates and suggest it is more useful for investigators to retain the existing, descriptive terms. When there are three or more risk categories, we recommend against net reclassification indices because they do not adequately account for clinically important differences in shifts among risk categories. The category-free net reclassification index is a new descriptive device designed to avoid pre-defined risk categories. However, it suffers from many of the same problems as other measures such as the area under the receiver operating characteristic curve. In addition, the category-free index can mislead investigators by overstating the incremental value of a biomarker, even in independent validation data. When investigators want to test a null hypothesis of no prediction increment, the well-established tests for coefficients in the regression model are superior to the net reclassification index. If investigators want to use net reclassification indices, confidence intervals should be calculated using bootstrap methods rather than published variance formulas. The preferred single-number summary of the prediction increment is the improvement in net benefit.

Rationale and Objectives Studies evaluating a new diagnostic imaging test may select control subjects without disease who are similar to case subjects with disease in regard to factors potentially related to the imaging result. Selecting one or more controls that are matched to each case on factors such as age, comorbidities, or study site improves study validity by eliminating potential biases due to differential characteristics of readings for cases versus controls. However, it is not widely appreciated that valid analysis requires that the receiver operating characteristic (ROC) curve be adjusted for covariates. We propose a new computationally simple method for estimating the covariate-adjusted ROC curve that is appropriate in matched case-control studies. Materials and Methods We provide theoretical arguments for the validity of the estimator and demonstrate its application to data. We compare the statistical properties of the estimator with those of a previously proposed estimator of the covariate-adjusted ROC curve. We demonstrate an application of the estimator to data derived from a study of emergency medical services encounters where the goal is to diagnose critical illness in nontrauma, non–cardiac arrest patients. A novel bootstrap method is proposed for calculating confidence intervals. Results The new estimator is computationally very simple, yet we show it yields values that approximate the existing, more complicated estimator in simulated data sets. We found that the new estimator has excellent statistical properties, with bias and efficiency comparable with the existing method. Conclusions In matched case-control studies, the ROC curve should be adjusted for matching covariates and can be estimated with the new computationally simple approach.

Two-phase study methods, in which more detailed or more expensive exposure information is only collected on a sample of individuals with events and a small proportion of other individuals, are expected to play a critical role in biomarker validation research. One major limitation of standard two-phase designs is that they are most conveniently employed with study cohorts in which information on longitudinal follow-up and other potential matching variables is electronically recorded. However for many practical situations, at the sampling stage such information may not be readily available for every potential candidates. Study eligibility needs to be verified by reviewing information from medical charts one by one. In this manuscript, we study in depth a novel study design commonly undertaken in practice that involves sampling until quotas of eligible cases and controls are identified. We propose semiparametric methods to calculate risk distributions and a wide variety of prediction indices when outcomes are censored failure times and data are collected under the quota sampling design. Consistency and asymptotic normality of our estimators are established using empirical process theory. Simulation results indicate that the proposed procedures perform well in finite samples. Application is made to the evaluation of a new risk model for predicting the onset of cardiovascular disease.

On 24 August 2016 (01:36 UTC) the intra-Apennine extensional fault system of Central Italy released a ML 6.0 destructiveearthquakebetweenthetownsofNorciaandAmatrice.Themainshockproducedwidespreaddamages and fatalities, devastating several localities and killing about 300 people. The main event was followed by a significant aftershock (ML 5.4), located 15 km to the NW. After one week, the epicentral area reached a length of about 30 km, in the NNW-SSE direction, largely developing at the hanging-wall of the WSW-dipping active extensional Vettore and Gorzano faults. The reconstructed 3D fault model consists in two major interconnected faultsegments,VettoreandGorzano,whichareindividualatdepthshallowerthanabout7-8kmandconvergeinto a unified surface at higher depths; the Vettore-Gorzano unified surface has a length of 65 km, dips WSW-ward with an angle of about 45-50o and reaches a depth of 11 km. By inverting the generated interferograms, following a classical Okada analytical approach, the best modelling results account for two sources related to main shock and more energetic aftershock. The time interval between theascendinganddescending(31August2016)doesnotdiscriminatetheeffectsderivedfromthemain24August event and by its aftershock, but the low magnitude of the second event can only very marginally contribute to the overall deformation pattern. Through Finite Element modelling that jointly exploits DInSAR deformation measurements and structuralgeological data, we reconstruct the 3D source of the Accumoli 2016 normal fault earthquake which fit better a moment Mw 6.2. As results of our modelling, we retrieve (i) the active seismogenic structures responsible for the observed ground deformation, (ii) the spatial distribution of the local stress field in term of volumetric stress and strain. The inversion shows that the co-seismic displacement area was partitioned on two distinct en echelon fault planes, which at the main event hypocentral depth (8 km) merge in one single WSW-dipping surface. Slip peaks were higher along the southern half of the Vettore fault, lower along the northern half of Gorzano fault and null in the relay zone between the two faults; field evidence of co-seismic surface rupture are coherent with the reconstructed scenario.

When an existing risk prediction model is not sufficiently predictive, additional variables are sought for inclusion in the model. This paper addresses study designs to evaluate the improvement in prediction performance that is gained by adding a new predictor to a risk prediction model. We consider studies that measure the new predictor in a case–control subset of the study cohort, a practice that is common in biomarker research. We ask if matching controls to cases in regards to baseline predictors improves efficiency. A variety of measures of prediction performance are studied. We find through simulation studies that matching improves the efficiency with which most measures are estimated, but can reduce efficiency for some. Efficiency gains are less when more controls per case are included in the study. A method that models the distribution of the new predictor in controls appears to improve estimation efficiency considerably.

When an existing standard marker does not have sufficient classification accuracy on its own, new markers are sought with the goal of yielding a combination with better performance. The primary criterion for selecting new markers is that they have good performance on their own and preferably be uncorrelated with the standard. Most often linear combinations are considered. In this paper, we investigate the increment in performance that is possible by combining a novel continuous marker with a moderately performing standard continuous marker under a variety of biologically motivated models for their joint distribution. We find that an uncorrelated continuous marker with moderate performance on its own usually yields only minimally improved performance. We identify other settings that lead to large improvements, including a novel marker that has very poor performance on its own but is highly correlated with the standard and a novel marker with poor to moderate performance that is highly correlated with the standard but only in one class category. These results suggest changing current strategies for identifying markers to be included in panels for possible combination. Using simulated and real datasets, we examine the merits of a broadened strategy that selects panels of markers as candidates on the basis of their joint performance with existing markers, compared with the standard strategy that selects markers on the basis of their marginal performance. We find that a broadened strategy can be fruitful but necessitates using studies with large numbers of subjects.

Authors have proposed new methodology in recent years for evaluating the improvement in prediction performance gained by adding a new predictor, Y, to a risk model containing a set of baseline predictors, X, for a binary outcome D. We prove theoretically that null hypotheses concerning no improvement in performance are equivalent to the simple null hypothesis that Y is not a risk factor when controlling for X, H0 : P(D = 1 | X,Y ) = P(D = 1 | X). Therefore, testing for improvement in prediction performance is redundant if Y has already been shown to be a risk factor. We also investigate properties of tests through simulation studies, focusing on the change in the area under the ROC curve (AUC). An unexpected finding is that standard testing procedures that do not adjust for variability in estimated regression coefficients are extremely conservative. This may explain why the AUC is widely considered insensitive to improvements in prediction performance and suggests that the problem of insensitivity has to do with use of invalid procedures for inference rather than with the measure itself. To avoid redundant testing and use of potentially problematic methods for inference, we recommend that hypothesis testing for no improvement be limited to evaluation of Y as a risk factor, for which methods are well developed and widely available. Analyses of measures of prediction performance should focus on estimation rather than on testing for no improvement in performance. Copyright © 2013 John Wiley & Sons, Ltd.

BACKGROUND The mission of the National Cancer Institute's Early Detection Research Network (EDRN) is to identify and validate cancer biomarkers for clinical use. Since its inception, EDRN investigators have learned a great deal about the process of validating biomarkers for clinical use. Translational research requires a broad spectrum of research expertise, and coordinating collaborative activities can be challenging. The EDRN has developed a robust triage and validation system that serves the roles of both “facilitator” and “brake.” CONTENT The system consists of (a) establishing a reference set of specimens collected under PRoBE (Prospective Specimen Collection Retrospective Blinded Evaluation) design criteria; (b) using the reference set to prevalidate candidate biomarkers before committing to full-scale validation; (c) performing full-scale validation for those markers that pass prevalidation testing; and (d) ensuring that the reference set is sufficiently large in numbers and volumes of sample that it can also be used to study future candidate biomarkers. This system provides rigorous and efficient evaluation of candidate biomarkers and biomarker panels. Reference sets should also be constructed to enable high-quality biomarker-discovery research. SUMMARY We describe the process of establishing our system in the hope that it will serve as an example of how to validate biomarkers for clinical application. We also hope that this description of the biospecimen reference sets available from the EDRN will encourage the biomarker research community—from academia or industry—to use this resource to advance biomarkers into clinical use.