Individual- and Group-Level Disparities Between Racial and Ethnic Groups in Lung Cancer Screening Eligibility Criteria.

Importance: Lung cancer screening guidelines result in differential screening eligibility among individuals who might benefit equally from screening and in population-level differences in screening eligibility and benefit across races and ethnicities.

Objective: To inform lung cancer screening policy development by evaluating how enforcing (1) equal lung cancer screening eligibility for all individuals with equal benefit and (2) equal program sensitivity across racial and ethnic subgroups are associated with screening eligibility and benefit.

Design, Setting, And Participants: This cross-sectional comparative effectiveness study included 6915 members of the US noninstitutionalized population aged 50 to 80 years who ever smoked and who participated in the 2015 National Health Interview Survey.

Sequential monitoring using the Second Generation P-Value with Type I error controlled by monitoring frequency.

The Second Generation P-Value (SGPV) measures the overlap between an estimated interval and a composite hypothesis of parameter values. We develop a sequential monitoring scheme of the SGPV (SeqSGPV) to connect study design intentions with end-of-study inference anchored on scientific relevance. We build upon Freedman's "Region of Equivalence" (ROE) in specifying scientifically meaningful hypotheses called Pre-specified Regions Indicating Scientific Merit (PRISM).

The Thoracic Research Evaluation and Treatment 2.0 Model: A Lung Cancer Prediction Model for Indeterminate Nodules Referred for Specialist Evaluation.

Background: Appropriate risk stratification of indeterminate pulmonary nodules (IPNs) is necessary to direct diagnostic evaluation. Currently available models were developed in populations with lower cancer prevalence than that seen in thoracic surgery and pulmonology clinics and usually do not allow for missing data. We updated and expanded the Thoracic Research Evaluation and Treatment (TREAT) model into a more generalized, robust approach for lung cancer prediction in patients referred for specialty evaluation.

Racial Disparities in Lung Cancer Stage of Diagnosis Among Adults Living in the Southeastern United States.

Background: Black Americans receive a diagnosis at later stage of lung cancer more often than White Americans. We undertook a population-based study to identify factors contributing to racial disparities in lung cancer stage of diagnosis among low-income adults.

Research Question: Which multilevel factors contribute to racial disparities in stage of lung cancer at diagnosis?

Study Design And Methods: Cases of incident lung cancer from the prospective observational Southern Community Cohort Study were identified by linkage with state cancer registries in 12 southeastern states.

FDRestimation: Flexible False Discovery Rate Computation in R.

False discovery rates (FDR) are an essential component of statistical inference, representing the propensity for an observed result to be mistaken. FDR estimates should accompany observed results to help the user contextualize the relevance and potential impact of findings. This paper introduces a new user-friendly R pack-age for estimating FDRs and computing adjusted p-values for FDR control.

Estimating the Posttest Probability of Long QT Syndrome Diagnosis for Rare Variants.

Background: The proliferation of genetic profiling has revealed many associations between genetic variations and disease. However, large-scale phenotyping efforts in largely healthy populations, coupled with DNA sequencing, suggest variants currently annotated as pathogenic are more common in healthy populations than previously thought. In addition, novel and rare variants are frequently observed in genes associated with disease both in healthy individuals and those under suspicion of disease.

Effect of balanced crystalloids versus saline on urinary biomarkers of acute kidney injury in critically ill adults.

Background: Recent trials have suggested use of balanced crystalloids may decrease the incidence of major adverse kidney events compared to saline in critically ill adults. The effect of crystalloid composition on biomarkers of early acute kidney injury remains unknown.

Methods: From February 15 to July 15, 2016, we conducted an ancillary study to the Isotonic Solutions and Major Adverse Renal Events Trial (SMART) comparing the effect of balanced crystalloids versus saline on urinary levels of neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) among 261 consecutively-enrolled critically ill adults admitted from the emergency department to the medical ICU.

A Bayesian method to estimate variant-induced disease penetrance.

A major challenge emerging in genomic medicine is how to assess best disease risk from rare or novel variants found in disease-related genes. The expanding volume of data generated by very large phenotyping efforts coupled to DNA sequence data presents an opportunity to reinterpret genetic liability of disease risk. Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant.

Semi-supervised Machine Learning with MixMatch and Equivalence Classes.

Semi-supervised methods have an increasing impact on computer vision tasks to make use of scarce labels on large datasets, yet these approaches have not been well translated to medical imaging. Of particular interest, the MixMatch method achieves significant performance improvement over popular semi-supervised learning methods with scarce labels in the CIFAR-10 dataset. In a complementary approach, Nullspace Tuning on equivalence classes offers the potential to leverage multiple subject scans when the ground truth for the subject is unknown.

Discovering novel disease comorbidities using electronic medical records.

Increasing reliance on electronic medical records at large medical centers provides unique opportunities to perform population level analyses exploring disease progression and etiology. The massive accumulation of diagnostic, procedure, and laboratory codes in one place has enabled the exploration of co-occurring conditions, their risk factors, and potential prognostic factors. While most of the readily identifiable associations in medical records are (now) well known to the scientific community, there is no doubt many more relationships are still to be uncovered in EMR data.

Evaluation of USPSTF Lung Cancer Screening Guidelines Among African American Adult Smokers.

Importance: The United States Preventive Services Task Force (USPSTF) recommends low-dose computed tomography screening for lung cancer. However, USPSTF screening guidelines were derived from a study population including only 4% African American smokers, and racial differences in smoking patterns were not considered.

Objective: To evaluate the diagnostic accuracy of USPSTF lung cancer screening eligibility criteria in a predominantly African American and low-income cohort.

A Regression Framework for Causal Mediation Analysis with Applications to Behavioral Science.

We introduce and extend the classical regression framework for conducting mediation analysis from the fit of only one model. Using the essential mediation components (EMCs) allows us to estimate causal mediation effects and their analytical variance. This single-equation approach reduces computation time and permits the use of a rich suite of regression tools that are not easily implemented on a system of three equations.

Protein structure aids predicting functional perturbation of missense variants in and .

Rare variants in the cardiac potassium channel K7.1 () and sodium channel Na1.5 () are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations.

Missing data and prediction: the pattern submodel.

Missing data are a common problem for both the construction and implementation of a prediction algorithm. Pattern submodels (PS)-a set of submodels for every missing data pattern that are fit using only data from that pattern-are a computationally efficient remedy for handling missing data at both stages. Here, we show that PS (i) retain their predictive accuracy even when the missing data mechanism is not missing at random (MAR) and (ii) yield an algorithm that is the most predictive among all standard missing data strategies.

Racial Disparities in Lung Cancer Survival: The Contribution of Stage, Treatment, and Ancestry.

Introduction: Lung cancer is a leading cause of cancer-related death worldwide. Racial disparities in lung cancer survival exist between blacks and whites, yet they are limited by categorical definitions of race. We sought to examine the impact of African ancestry on overall survival among blacks and whites with NSCLC cases.

(Na1.5) Variant Functional Perturbation and Clinical Presentation: Variants of a Certain Significance.

Background: Accurately predicting the impact of rare nonsynonymous variants on disease risk is an important goal in precision medicine. Variants in the cardiac sodium channel (protein Na1.5; voltage-dependent cardiac Na+ channel) are associated with multiple arrhythmia disorders, including Brugada syndrome and long QT syndrome.

Second-generation p-values: Improved rigor, reproducibility, & transparency in statistical analyses.

Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we introduce a novel extension of the p-value-a second-generation p-value (pδ)-that formally accounts for scientific relevance and leverages this natural Type I Error control. The approach relies on a pre-specified interval null hypothesis that represents the collection of effect sizes that are scientifically uninteresting or are practically null.

A general approach to risk modeling using partial surrogate markers with application to perioperative acute kidney injury.

Background: Surrogate outcomes are often utilized when disease outcomes are difficult to directly measure. When a biological threshold effect exists, surrogate outcomes may only represent disease in specific subpopulations. We refer to these outcomes as "partial surrogate outcomes.

High-Density Lipoprotein Cholesterol Concentration and Acute Kidney Injury After Cardiac Surgery.

Background: Acute kidney injury (AKI) after cardiac surgery is associated with increased short- and long-term mortality. Inflammation, oxidative stress, and endothelial dysfunction and damage play important roles in the development of AKI. High-density lipoproteins (HDLs) have anti-inflammatory and antioxidant properties and improve endothelial function and repair.

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis.

This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis.

Assessment of Fluorodeoxyglucose F18-Labeled Positron Emission Tomography for Diagnosis of High-Risk Lung Nodules.

Importance: Clinicians rely heavily on fluorodeoxyglucose F18-labeled positron emission tomography (FDG-PET) imaging to evaluate lung nodules suspicious for cancer. We evaluated the performance of FDG-PET for the diagnosis of malignancy in differing populations with varying cancer prevalence.

Objective: To determine the performance of FDG-PET/computed tomography (CT) in diagnosing lung malignancy across different populations with varying cancer prevalence.

A classical regression framework for mediation analysis: fitting one model to estimate mediation effects.

Mediation analysis explores the degree to which an exposure's effect on an outcome is diverted through a mediating variable. We describe a classical regression framework for conducting mediation analyses in which estimates of causal mediation effects and their variance are obtained from the fit of a single regression model. The vector of changes in exposure pathway coefficients, which we named the essential mediation components (EMCs), is used to estimate standard causal mediation effects.

Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance.

Background: An emerging standard-of-care for long-QT syndrome uses clinical genetic testing to identify genetic variants of the KCNQ1 potassium channel. However, interpreting results from genetic testing is confounded by the presence of variants of unknown significance for which there is inadequate evidence of pathogenicity.

Methods And Results: In this study, we curated from the literature a high-quality set of 107 functionally characterized KCNQ1 variants.