Jeffrey D. Blume

Background: The US Preventive Services Task Force (USPSTF) lung cancer screening eligibility guidelines and proposed risk models have been developed using data predominantly from White populations. Studies show that these eligibility strategies perform inconsistently across racially diverse populations, suggesting evidence of algorithmic bias. We assessed several lung cancer screening eligibility strategies and explored how algorithmic bias can be resolved to improve equity in eligibility. Methods: Using the Southern Community Cohort Study, a large US study of predominantly Black/African American individuals, we evaluated the performance of 8 existing lung cancer screening eligibility strategies (USPSTF 2021; American Cancer Society 2023 recommendations; USPSTFSmokeDuration; Prostate, Lung, Colorectal and Ovarian 2012 risk prediction model [PLCOm2012]; PLCOm2012NoRace; PLCOm2012Update; Lung Cancer Risk Assessment Tool; and Lung Cancer Death Risk Assessment tool) and 2 new race-aware strategies proposed by our team (USPSTFRaceSpecific and PLCOm2012RaceSpecific). Results: Among 52 667 adults (65% Black/African American, 31% White, 4% Multiracial/Other) with a smoking history, 1689 developed lung cancer over 15 years. Most screening strategies identified fewer Black/African American participants who developed lung cancer as eligible for screening vs their White counterparts (sensitivity for Black/African American individuals = 0.46-0.73 vs 0.72-0.80 for their White counterparts). Racial eligibility disparities were not resolved by removing race, removing the “years since quit” criterion, or using uniform risk thresholds. Replacing pack-years with smoking duration improved equity but overinflated the false-positive rate (0.71 for Black/African American persons vs 0.61 for White persons). Instead, race-aware approaches that tailored eligibility thresholds by race yielded the best sensitivity-specificity trade-off and minimized inequities (sensitivity = 0.71-0.73 for Black/African American persons vs 0.72-0.74 for White persons; false-positive rate = 0.49-0.50 for Black/African American persons vs 0.50-0.53 for White persons). Conclusion: Our findings suggest that race-aware approaches are necessary to address algorithmic bias and ensure equitable opportunities for lung cancer screening.

Changes in science practices are often perceived to be slow. It took about 10 years from the Collins and Tabak editorial on scientific reproducibility in 2014 [1] to see data management mandates implemented by US funding agencies [2]. However, open science practices have seen a sharp increase in adoption over the last few years, supported by policy (for example, those by the European Commission or the 2022 White House Office of Science and Technology Policy (OSTP) memoAU : Pleasecheckandconfirmthattheplacementofclosingparenthesisinthesenten) [3,4] as well as new generations of digital tools and scientists who are embedding open values in their research practices. In this faster-paced open science environment, universities are key to fostering adoption among researchers. Universities drive implementation by advancing best practices and accounting for the needs and norms of diverse departments and disciplines. Universities are positioned to catalyze adoption of open practices through their academic evaluation processes, particularly, recruitment, tenure, and promotion. The capacity of researchers and instructors to engage with data and software scholarship will shape the next generation of students and scientists, and universities will play a crucial role in nurturing those skills by rewarding such contributions and expertise among their faculty. The ways in which promotion and tenure committees operate vary significantly across universities and departments. While committees often have the capability to evaluate the rigor and quality of articles and monographs in their scientific field, assessment with respect to practices concerning research data and software is a recent development and one that can be harder to implement, as there are few guidelines to facilitate the process. More specifically, the guidelines given to tenure and promotion committees often reference data and software in general terms, with some notable exceptions such as guidelines in [5] and are almost systematically trumped by other factors such as the number and perceived impact of journal publications. The core issue is that many colleges establish a scholarship versus service dichotomy:

Purpose: Black Americans experience poorer lung cancer survival than White Americans. Racial disparities in stage at diagnosis may contribute to these survival differences, but few studies have explored factors leading to racial disparities in lung cancer stage at diagnosis. We aimed to identify multilevel factors contributing to racial disparities in stage of lung cancer presentation. Methods: Using data from the Southern Community Cohort Study (SCCS), we examined factors associated with distant stage among adults diagnosed with incident lung cancer. SCCS participants were prospectively enrolled, primarily from community health centers between 2002 and 2009 across a 12-state area. Incident cancers were identified by linkage with state cancer registries through end of follow-up in 2019. Self-reported social, behavioral, and medical history information were ascertained at baseline via questionnaire. Cumulative exposure smoking histories were identified using the most recent follow-up questionnaires. Residential addresses and National Cancer Institute Comprehensive Cancer Center locations were geocoded, and residential addresses were linked to census data. Logistic and multinomial regression models were used to identify factors predictive of distant stage diagnosis. Penalized regression was used to shrink the predictor space of these models when necessary. Findings were replicated in an independent population. Results: Among 1,672 incident SCCS lung cancer cases (35% White, 61% Black, and 3% other self-reported race), a greater percentage of Black participants than White participants were diagnosed with distant stage lung cancer (56.4% vs 49.4%, respectively). Overall, Black participants had greater odds of distant vs local stage compared to White participants (odds ratio (OR) = 1.28, 95% confidence interval (CI): 1.05-1.58). Greater area deprivation was also associated with distant lung cancer stage (OR = 1.55, 95% CI: 1.17-2.04). After controlling for individual and area-level factors, there was no significant difference in the odds of distant stage disease for Black participants compared to White participants (OR = 1.03, 95% CI: 0.80-1.33). Significant interactions between race and area deprivation index were not observed. However, greater residential distance from a comprehensive cancer center was significantly associated with increased odds of distant stage disease in the final model (OR = 1.04, 95% CI: 1.00-1.08). No significant differences were observed in the odds of distant stage lung cancer among Black and White participants in the independent population. Conclusions: A greater percentage of Black participants were diagnosed with distant stage lung cancer; however, this disparity dissipated after adjusting for individual and area-level factors. Our findings suggest racial disparities in lung cancer stage at diagnosis may be ameliorated with modifiable factors, such as patient access to high quality cancer centers. Citation Format: Jennifer Richmond, Megan Hollister, Cato M. Milder, Ann G. Schwartz, Jeffrey D. Blume, Melinda C. Aldrich. Examining racial disparities in lung cancer stage of diagnosis among low-income adults living in the southeastern U.S. [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-236.

Many statistical methods have been proposed for variable selection in the past century, but few balance inference and prediction tasks well. Here we report on a novel variable selection approach called Penalized regression with Second-Generation P-Values (ProSGPV). It captures the true model at the best rate achieved by current standards, is easy to implement in practice, and often yields the smallest parameter estimation error. The idea is to use an l0 penalization scheme with second-generation p-values (SGPV), instead of traditional ones, to determine which variables remain in a model. The approach yields tangible advantages for balancing support recovery, parameter estimation, and prediction tasks. The ProSGPV algorithm can maintain its good performance even when there is strong collinearity among features or when a high dimensional feature space with p > n is considered. We present extensive simulations and a real-world application comparing the ProSGPV approach with smoothly clipped absolute deviation (SCAD), adaptive lasso (AL), and mini-max concave penalty with penalized linear unbiased selection (MC+). While the last three algorithms are among the current standards for variable selection, ProSGPV has superior inference performance and comparable prediction performance in certain scenarios. Supplementary materials are available online.

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 package for computing FDRs and adjusting p-values for FDR control. These tools respect the critical difference between the adjusted p-value and the estimated FDR for a particular finding, which are sometimes numerically identical but are often confused in practice. Newly augmented methods for estimating the null proportion of findings - an important part of the FDR estimation procedure - are proposed and evaluated. The package is broad, encompassing a variety of methods for FDR estimation and FDR control, and includes plotting functions for easy display of results. Through extensive illustrations, we strongly encourage wider reporting of false discovery rates for observed findings.

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. We refer to this as the penetrance, the fraction of all variant heterozygotes that will present with disease. We demonstrate this methodology using a well-established disease-gene pair, the cardiac sodium channel gene SCN5A and the heart arrhythmia Brugada syndrome. From a review of 756 publications, we developed a pattern mixture algorithm, based on a Bayesian Beta-Binomial model, to generate SCN5A penetrance probabilities for the Brugada syndrome conditioned on variant-specific attributes. These probabilities are determined from variant-specific features (e.g. function, structural context, and sequence conservation) and from observations of affected and unaffected heterozygotes. Variant functional perturbation and structural context prove most predictive of Brugada syndrome penetrance. The clinical implications for genetic variants, even definitively pathogenic variants, can vary strikingly across individuals. Lack of evidence to estimate the probability of disease from identified genetic variants, especially rare variants, presents a major barrier to integrating genotype information into clinical care. Here we advance an approach to estimate the penetrance, or positive predictive value of the discovery of a genetic variant, in service of advancing the use of genetic information in personalized medicine.