Search

The heart transplantation policy change (PC) has improved outcomes in high-acuity (Old 1A, New 1-3) patients, but the effect on low-priority (Old 1B/2, New 4-6) patients is unknown. We sought to determine if low-priority patient outcomes were compromised by benefits to high-priority patients by evaluating for interaction between PC and priority status (PS). We included adult first-time heart transplant candidates and recipients from the UNOS registry during a 19-month period before and after the PC. We compared clinical characteristics and performed competing risks and survival analyses stratified by PC and PS. There was a dependence of PC and PS on waitlist death/deterioration with an interaction sub-distribution hazard ratio (adjusted sdHR) of 0.59 (0.45-0.78), p-value .001. There was a trend toward a benefit of PC on waitlist death/deterioration (adjusted sdHR: 0.86 [0.73-1.01]; p = .07) and an increase in heart transplantation (adjusted sdHR: 1.08 [1.02-1.14], p = .007) for low-priority patients. There was no difference in 1-year post-transplant survival (log-rank p = .22) when stratifying by PC and PS. PC did not negatively affect waitlisted or transplanted low-priority patients. High-priority, post-PC patients had a targeted reduction in waitlist death/deterioration and did not come at the expense of worse post-transplant survival.

There is a great deal of prior knowledge about gene function and regulation in the form of annotations or prior results that, if directly integrated into individual prognostic or diagnostic studies, could improve predictive performance. For example, in a study to develop a predictive model for cancer survival based on gene expression, effect sizes from previous studies or the grouping of genes based on pathways constitute such prior knowledge. However, this external information is typically only used post-analysis to aid in the interpretation of any findings. We propose a new hierarchical two-level ridge regression model that can integrate external information in the form of “meta features” to predict an outcome. We show that the model can be fit efficiently using cyclic coordinate descent by recasting the problem as a single-level regression model. In a simulation-based evaluation we show that the proposed method outperforms standard ridge regression and competing methods that integrate prior information, in terms of prediction performance when the meta features are informative on the mean of the features, and that there is no loss in performance when the meta features are uninformative. We demonstrate our approach with applications to the prediction of chronological age based on methylation features and breast cancer mortality based on gene expression features.

In this article, we develop a new l0-based sparse Poisson graphical model with applications to gene network inference from RNA-seq gene expression count data. Assuming a pair-wise Markov property, ...

Purpose The aim of the study was to determine the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in a university student population. Methods This was a cross-sectional survey study based on the World Health Organization population-based seroepidemiological investigational protocol for SARS-CoV-2 conducted between April 29, 2020, and May 8, 2020, examining SARS-CoV-2 antibody prevalence among 790 university students in Los Angeles, CA. Participants completed a questionnaire on potential risk factors before blood sampling. Samples were analyzed using the EUROIMMUN Anti-SARS-CoV-2 ELISA (IgG) for the qualitative detection of IgG class antibodies to SARS-CoV-2 in human serum or plasma. Results The estimated prevalence of SARS-CoV-2 antibody was 4.0% (3.0%, 5.1%). Factors associated with having a positive test included history of anosmia and/or loss of taste (95% CI: 1.4–9.6). A history of respiratory symptoms, with or without fever, was not associated with a positive antibody test. Conclusions Prevalence of SARS-CoV-2 antibodies in the undergraduate and graduate student university population was similar to community prevalence.

Sparse high-dimensional massive sample size (sHDMSS) time-to-event data present multiple challenges to quantitative researchers as most current sparse survival regression methods and software will grind to a halt and become practically inoperable. This paper develops a scalable l0 -based sparse Cox regression tool for right-censored time-to-event data that easily takes advantage of existing high performance implementation of l2 -penalized regression method for sHDMSS time-to-event data. Specifically, we extend the l0 -based broken adaptive ridge (BAR) methodology to the Cox model, which involves repeatedly performing reweighted l2 -penalized regression. We rigorously show that the resulting estimator for the Cox model is selection consistent, oracle for parameter estimation, and has a grouping property for highly correlated covariates. Furthermore, we implement our BAR method in an R package for sHDMSS time-to-event data by leveraging existing efficient algorithms for massive l2 -penalized Cox regression. We evaluate the BAR Cox regression method by extensive simulations and illustrate its application on an sHDMSS time-to-event data from the National Trauma Data Bank with hundreds of thousands of observations and tens of thousands sparsely represented covariates.

IntroductionAlthough COVID-19 vaccines significantly reduce morbidity and mortality, recent evidence suggests that immunity wanes after 6-9 months, and that a third vaccine dose could further reduce COVID-19 transmission and severe illness. However, previous studies have not assessed attitudes about getting booster doses. This study examined COVID-19 booster vaccine attitudes and behaviors among university students and staff in the fall of 2021.MethodsParticipants responded to an email invitation and completed electronic surveys. Results. In our sample, 96.2% of respondents indicated willingness to get a COVID-19 booster shot at least once per year. In both bivariate and multivariate analyses higher trust in science was associated with having higher odds of booster willingness. Those who identify as Black, on average, reported trusting science less than other racial/ethnic groups.ConclusionsOur findings demonstrate high willingness to receive a COVID-19 booster shot and highlight the importance of educational and motivational messages that focus on trust in science to increase willingness to get the COVID-19 booster. More research is needed to better understand the impact of cultural beliefs on booster willingness and vaccine hesitancy. This understanding will help determine what messages and populations to target to increase booster willingness in the future.

Defined by their genetic profile, individuals may exhibit differential clinical outcomes due to an environmental exposure. Identifying subgroups based on specific exposure-modifying genes can lead to targeted interventions and focused studies. Genome-wide interaction scans (GWIS) can be performed to identify such genes, but these scans typically suffer from low power due to the large multiple testing burden. We provide a novel framework for powerful two-step hypothesis tests for GWIS with a time-to-event endpoint under the Cox proportional hazards model. In the Cox regression setting, we develop an approach that prioritizes genes for Step-2 G × E testing based on a carefully constructed Step-1 screening procedure. Simulation results demonstrate this two-step approach can lead to substantially higher power for identifying gene-environment ( G × E ) interactions compared to the standard GWIS while preserving the family-wise error rate over a range of scenarios. In a taxane-anthracycline chemotherapy study for breast cancer patients, the two-step approach identifies several gene expression by treatment interactions that would not be detected using the standard GWIS.

ObjectiveThis study examined characteristics associated with being unvaccinated among a sample of university staff and faculty prior to university campus reopening for in-person learning in spring-summer 2021.MethodsStaff and faculty responded to an email invitation to complete an online survey. Survey questions included demographic data (race/ethnicity, age, sex), COVID-19 knowledge and behaviors, employment specific data including division and subdivision (healthcare vs. non-healthcare related division); and self-reported vaccination status. A multivariable logistic regression analysis was performed to determine significant characteristics associated with the likelihood of being unvaccinated for COVID-19.ResultsParticipants identifying as Asian and Asian American, Hispanic/Latinx or Multicultural/Other had greater odds of being unvaccinated compared to Non-Hispanic White participants. Other characteristics associated with greater likelihood of being unvaccinated included working as university staff member (vs. faculty), older age, decrease in income, inability to work remotely and not traveling outside of Los Angeles area. Political affiliation as an Independent or as something else were more likely to be unvaccinated compared to participants identifying as Democrat.ConclusionsFindings suggest several factors associated with racial and social disparities may delay the uptake of COVID-19 vaccination. This study highlights the need for targeted educational interventions to promote vaccination among university staff and faculty.

ObjectivesDespite the widespread availability of COVID-19 vaccines in the United States, vaccine hesitancy remains high among certain groups. This study examined the correlates of being unvaccinated among a sample of university students (N=2900) during the spring and summer of 2021, when the campus had been closed for over a year and students were preparing to return to in-person learning.MethodsStudents responded to an email invitation and completed electronic surveys. Results. In multivariable logistic regression analyses, students were more likely to be unvaccinated if they were African American, identified with any political affiliation other than Democrat, were undergraduates or international students, had not traveled outside the Los Angeles during the pandemic, and/or had previously been ill with COVID-19.ConclusionFindings indicate that culturally resonant educational interventions, and possibly vaccine requirements, are needed to promote vaccination among university students.

In this article, we develop a new ℓ ( 0 ) -based sparse Poisson graphical model with applications to gene network inference from RNA-seq gene expression count data. Assuming a pair-wise Markov property, we propose to fit a separate broken adaptive ridge-regularized log-linear Poisson regression on each node to evaluate the conditional, instead of marginal, association between two genes in the presence of all other genes. The resulting sparse gene networks are generally more accurate than those generated by the ℓ ( 1 ) -regularized Poisson graphical model as demonstrated by our empirical studies. A real data illustration is given on a kidney renal clear cell carcinoma micro-RNA-seq data from the Cancer Genome Atlas.

Background The number of bariatric surgeries performed in the United States has increased substantially since the 1990’s. However, the prevalence and prognostic impact of bariatric surgery, or weight loss surgery (WLS), among patients with cancer are not known. Objectives We investigated the population-based prevalence of WLS in women with breast or endometrial cancer and conducted exploratory analysis to examine whether postdiagnosis WLS is associated with survival. Setting Administrative statewide database. Methods WLS records for women with nonmetastasized breast (n = 395,146) or endometrial (n = 69,859) cancer were identified from the 1991–2014 California Cancer Registry data linked with the California Office of Statewide Health Planning and Development database. Characteristics of the patients were examined according to history of WLS. Using body mass index data available since 2011, a retrospective cohort of patients with breast or endometrial cancer and obesity (n = 12,540) was established and followed until 2017 (5% lost to follow-up). Multivariable cause-specific Cox proportional hazards models were used to examine the associations between postdiagnostic WLS and time to death. Results WLS records were identified for 2844 (.7%) patients with breast cancer and 1140 (1.6%) patients with endometrial cancer; about half of the surgeries were performed after cancer diagnosis. Postdiagnosis WLS was performed in ∼1% of patients with obesity and was associated with a decreased hazard for death (cause-specific hazard ratio = .37; 95% confidence interval = .014–.99; P = .049), adjusting for age, stage, co-morbidity, race/ethnicity, and socioeconomic status. Conclusion About 2000 patients with breast or endometrial cancer in California underwent post-diagnosis WLS between 1991 and 2014. Our data support survival benefits of WLS after breast and endometrial cancer diagnosis.

Penalized regression provides an automated approach to preform simultaneous variable selection and parameter estimation and is a popular method to analyze high-dimensional data. Since the conception of the LASSO in the mid-to-late 1990s, extensive research has been done to improve penalized regression. The LASSO, and several of its variations, performs penalization symmetrically around zero. Thus, variables with the same magnitude are shrunk the same regardless of the direction of effect. To the best of our knowledge, sign-based shrinkage, preferential shrinkage based on the sign of the coefficients, has yet to be explored under the LASSO framework. We propose a generalization to the LASSO, asymmetric LASSO, that performs sign-based shrinkage. Our method is motivated by placing an asymmetric Laplace prior on the regression coefficients, rather than a symmetric Laplace prior. This corresponds to an asymmetric ℓ(1) penalty under the penalized regression framework. In doing so, preferential shrinkage can be performed through an auxiliary tuning parameter that controls the degree of asymmetry. Our numerical studies indicate that the asymmetric LASSO performs better than the LASSO when effect sizes are sign skewed. Furthermore, in the presence of positively-skewed effects, the asymmetric LASSO is comparable to the non-negative LASSO without the need to place an a priori constraint on the effect estimates and outperforms the non-negative LASSO when negative effects are also present in the model. A real data example using the breast cancer gene expression data from The Cancer Genome Atlas is also provided, where the asymmetric LASSO identifies two potentially novel gene expressions that are associated with BRCA1 with a minor improvement in prediction performance over the LASSO and non-negative LASSO.

This paper develops two orthogonal contributions to scalable sparse regression for competing risks time-to-event data. First, we study and accelerate the broken adaptive ridge method (BAR), a surrogate ℓ(0)-based iteratively reweighted ℓ(2)-penalization algorithm that achieves sparsity in its limit, in the context of the Fine-Gray (1999) proportional subdistributional hazards (PSH) model. In particular, we derive a new algorithm for BAR regression, named cycBAR, that performs cyclic update of each coordinate using an explicit thresholding formula. The new cycBAR algorithm effectively avoids fitting multiple reweighted ℓ(2)-penalizations and thus yields impressive speedups over the original BAR algorithm. Second, we address a pivotal computational issue related to fitting the PSH model. Specifically, the computation costs of the log-pseudo likelihood and its derivatives for PSH model grow at the rate of O(n(2)) with the sample size n in current implementations. We propose a novel forward-backward scan algorithm that reduces the computation costs to O(n). The proposed method applies to both unpenalized and penalized estimation for the PSH model and has exhibited drastic speedups over current implementations. Finally, combining the two algorithms can yields > 1, 000 fold speedups over the original BAR algorithm. Illustrations of the impressive scalability of our proposed algorithm for large competing risks data are given using both simulations and a United States Renal Data System data. Supplementary materials for this article are available online.

Sparse high-dimensional massive sample size (sHDMSS) time-to-event data present multiple challenges to quantitative researchers as most current sparse survival regression methods and software will grind to a halt and become practically inoperable. This paper develops a scalable ℓ(0)-based sparse Cox regression tool for right-censored time-to-event data that easily takes advantage of existing high performance implementation of ℓ(2)-penalized regression method for sHDMSS time-to-event data. Specifically, we extend the ℓ(0)-based broken adaptive ridge (BAR) methodology to the Cox model, which involves repeatedly performing reweighted ℓ(2)-penalized regression. We rigorously show that the resulting estimator for the Cox model is selection consistent, oracle for parameter estimation, and has a grouping property for highly correlated covariates. Furthermore, we implement our BAR method in an R package for sHDMSS time-to-event data by leveraging existing efficient algorithms for massive ℓ(2)-penalized Cox regression. We evaluate the BAR Cox regression method by extensive simulations and illustrate its application on an sHDMSS time-to-event data from the National Trauma Data Bank with hundreds of thousands of observations and tens of thousands sparsely represented covariates.

Glioblastoma is the most common primary malignant brain tumor in adults and is associated with poor survival. The Ivy Foundation Early Phase Clinical Trials Consortium conducted a randomized, multi-institution clinical trial to evaluate immune responses and survival following neoadjuvant and/or adjuvant therapy with pembrolizumab in 35 patients with recurrent, surgically resectable glioblastoma. Patients who were randomized to receive neoadjuvant pembrolizumab, with continued adjuvant therapy following surgery, had significantly extended overall survival compared to patients that were randomized to receive adjuvant, post-surgical programmed cell death protein 1 (PD-1) blockade alone. Neoadjuvant PD-1 blockade was associated with upregulation of T cell- and interferon-γ-related gene expression, but downregulation of cell-cycle-related gene expression within the tumor, which was not seen in patients that received adjuvant therapy alone. Focal induction of programmed death-ligand 1 in the tumor microenvironment, enhanced clonal expansion of T cells, decreased PD-1 expression on peripheral blood T cells and a decreasing monocytic population was observed more frequently in the neoadjuvant group than in patients treated only in the adjuvant setting. These findings suggest that the neoadjuvant administration of PD-1 blockade enhances both the local and systemic antitumor immune response and may represent a more efficacious approach to the treatment of this uniformly lethal brain tumor.

Advancements in medical informatics tools and high-throughput biological experimentation make large-scale biomedical data routinely accessible to researchers. Competing risks data are typical in biomedical studies where individuals are at risk to more than one cause (type of event) which can preclude the others from happening. The Fine-Gray model is a popular and well-appreciated model for competing risks data and is currently implemented in a number of statistical software packages. However, current implementations are not computationally scalable for large-scale competing risks data. We have developed an R package, fastcmprsk, that uses a novel forward-backward scan algorithm to significantly reduce the computational complexity for parameter estimation by exploiting the structure of the subject-specific risk sets. Numerical studies compare the speed and scalability of our implementation to current methods for unpenalized and penalized Fine-Gray regression and show impressive gains in computational efficiency., Comment: 21 pages; 5 figures; 3 tables