An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

Societal determinants of flood-induced displacement.

What explains human consequences of weather-related disaster? Here, we explore how core socioeconomic, political, and security conditions shape flood-induced displacement worldwide since 2000. In-sample regression analysis shows that extreme displacement levels are more likely in contexts marked by low national income levels, nondemocratic political systems, high local economic activity, and prevalence of armed conflict. The analysis also reveals large residual differences across continents, where flood-induced displacement in the Global South often is much more widespread than direct human exposure measures would suggest. However, these factors have limited influence on our ability to accurately predict flood displacement on new data, pointing to important, hard-to-operationalize heterogeneity in flood impacts across contexts and critical data limitations. Although results are consistent with an interpretation that the sustainable development agenda is beneficial for disaster risk reduction, better data on societal consequences of natural hazards are critically needed to support evidence-based decision-making.

Large-scale climate patterns offer preseasonal hints on the co-occurrence of heat wave and O3 pollution in China.

Heat waves and air pollution extremes exert compounding effects on human health and food security and may worsen under future climate change. On the basis of reconstructed daily O3 levels in China and meteorological reanalysis, we found that the interannual variability of the frequency of summertime co-occurrence of heat wave and O3 pollution in China is regulated mainly by a combination of springtime warming in the western Pacific Ocean, western Indian Ocean, and Ross Sea. These sea surface temperature anomalies impose influences on precipitation, radiation, etc., to modulate the co-occurrence, which were also confirmed with coupled chemistry-climate numerical experiments. We thus built a multivariable regression model to predict co-occurrence a season in advance, and correlation coefficient could reach 0.81 (P < 0.01) for the North China Plain. Our results provide useful information for the government to take actions in advance to mitigate damage from these synergistic costressors.

Pooling controls from nested case-control studies with the proportional risks model.

The standard approach to regression modeling for cause-specific hazards with prospective competing risks data specifies separate models for each failure type. An alternative proposed by Lunn and McNeil (1995) assumes the cause-specific hazards are proportional across causes. This may be more efficient than the standard approach, and allows the comparison of covariate effects across causes. In this paper, we extend Lunn and McNeil (1995) to nested case-control studies, accommodating scenarios with additional matching and non-proportionality. We also consider the case where data for different causes are obtained from different studies conducted in the same cohort. It is demonstrated that while only modest gains in efficiency are possible in full cohort analyses, substantial gains may be attained in nested case-control analyses for failure types that are relatively rare. Extensive simulation studies are conducted and real data analyses are provided using the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) study.

Updated concepts of seismic gaps and asperities to assess great earthquake hazard along South America.

So far in this century, six very large-magnitude earthquakes (MW ≥ 7.8) have ruptured separate portions of the subduction zone plate boundary of western South America along Ecuador, Peru, and Chile. Each source region had last experienced a very large earthquake from 74 to 261 y earlier. This history led to their designation in advance as seismic gaps with potential to host future large earthquakes. Deployments of geodetic and seismic monitoring instruments in several of the seismic gaps enhanced resolution of the subsequent faulting processes, revealing preevent patterns of geodetic slip deficit accumulation and heterogeneous coseismic slip on the megathrust fault. Localized regions of large slip, or asperities, appear to have influenced variability in how each source region ruptured relative to prior events, as repeated ruptures have had similar, but not identical slip distributions. We consider updated perspectives of seismic gaps, asperities, and geodetic locking to assess current very large earthquake hazard along the South American subduction zone, noting regions of particular concern in northern Ecuador and Colombia (1958/1906 rupture zone), southeastern Peru (southeasternmost 1868 rupture zone), north Chile (1877 rupture zone), and north-central Chile (1922 rupture zone) that have large geodetic slip deficit measurements and long intervals (from 64 to 154 y) since prior large events have struck those regions. Expanded geophysical measurements onshore and offshore in these seismic gaps may provide critical information about the strain cycle and fault stress buildup late in the seismic cycle in advance of the future great earthquakes that will eventually strike each region.

Mapping the Ecological Terrain of Stroke Prehospital Delay: A Nationwide Registry Study.

BACKGROUND: Delays in hospital presentation limit access to acute stroke treatments. While prior research has focused on patient-level factors, broader ecological and social determinants have not been well studied. We aimed to create a geospatial map of prehospital delay and examine the role of community-level social vulnerability. METHODS: We studied patients with ischemic stroke who arrived by emergency medical services in 2015 to 2017 from the American Heart Association Get With The Guidelines-Stroke registry. The primary outcome was time to hospital arrival after stroke (in minutes), beginning at last known well in most cases. Using Geographic Information System mapping, we displayed the geography of delay. We then used Cox proportional hazard models to study the relationship between community-level factors and arrival time (adjusted hazard ratios [aHR] <1.0 indicate delay). The primary exposure was the social vulnerability index (SVI), a metric of social vulnerability for every ZIP Code Tabulation Area ranging from 0.0 to 1.0. RESULTS: Of 750 336 patients, 149 145 met inclusion criteria. The mean age was 73 years, and 51% were female. The median time to hospital arrival was 140 minutes (Q1: 60 minutes, Q3: 458 minutes). The geospatial map revealed that many zones of delay overlapped with socially vulnerable areas (https://harvard-cga.maps.arcgis.com/apps/webappviewer/index.html?id=08f6e885c71b457f83cefc71013bcaa7). Cox models (aHR, 95% CI) confirmed that higher SVI, including quartiles 3 (aHR, 0.96 [95% CI, 0.93-0.98]) and 4 (aHR, 0.93 [95% CI, 0.91-0.95]), was associated with delay. Patients from SVI quartile 4 neighborhoods arrived 15.6 minutes [15-16.2] slower than patients from SVI quartile 1. Specific SVI themes associated with delay were a community's socioeconomic status (aHR, 0.80 [95% CI, 0.74-0.85]) and housing type and transportation (aHR, 0.89 [95% CI, 0.84-0.94]). CONCLUSIONS: This map of acute stroke presentation times shows areas with a high incidence of delay. Increased social vulnerability characterizes these areas. Such places should be systematically targeted to improve population-level stroke presentation times.

Residential natural hazard risk and mental health effects.

Mental health effects are frequently reported following natural disasters. However, little is known about effects of living in a hazard-prone region on mental health. We analyzed data from 9,312 Gulf Long-term Follow-up Study participants who completed standardized mental health questionnaires including the Patient Health Questionnaire-9 (depression=score≥10), Generalized Anxiety Disorder Questionnaire-7 (anxiety=score≥10), and Primary Care PTSD Screen (PTSD=score≥3). Geocoded residential addresses were linked to census-tract level natural hazard risk scores estimated using the National Risk Index (NRI). We considered an overall risk score representing 18 natural hazards, and individual scores for hurricanes, heatwaves, coastal flooding and riverine flooding. Log binomial regression estimated prevalence ratios (PR) and 95% confidence intervals (CI) for associations between risk scores (quartiles) and mental health outcomes. Increasing hurricane and coastal flooding scores were associated with all mental health outcomes in a suggestive exposure-response manner. Associations were strongest for PTSD, with PRs for the highest vs. lowest quartile of hurricane and coastal flooding risks of 2.29(1.74-3.01) and 1.59(1.23-2.05), respectively. High heatwave risk was associated with anxiety (PR=1.25(1.12-1.38)) and depression (PR=1.19(1.04-1.36)) and suggestively with PTSD (PR=1.20(0.94-1.52)). Results suggest that living in areas prone to natural disasters is one factor associated with poor mental health status.

A simple Cox approach to estimating risk ratios without sharing individual-level data in multi-site studies.

Epidemiologic studies frequently use risk ratios to quantify associations between exposures and binary outcomes. When the data are physically stored at multiple data partners, it can be challenging to perform individual-level analysis if data cannot be pooled centrally due to privacy constraints. Existing methods either require multiple file transfers between each data partner and an analysis center (e.g., distributed regression) or only provide approximate estimation of the risk ratio (e.g., meta-analysis). Here we develop a practical method that requires a single transfer of eight summary-level quantities from each data partner. Our approach leverages an existing risk-set method and software originally developed for Cox regression. Sharing only summary-level information, the proposed method provides risk ratio estimates and confidence intervals identical to those that would be provided - if individual-level data were pooled - by the modified Poisson regression. We justify the method theoretically, confirm its performance using simulated data, and implement it in a distributed analysis of COVID-19 data from the U.S. Food and Drug Administration's Sentinel System.

Reappraisal of prognostic factors used in the European Pediatric Soft Tissue Sarcoma Study Group RMS 2005 study for localized rhabdomyosarcoma to optimize risk stratification and generate a prognostic nomogram.

BACKGROUND: The objective of this study was to investigate the role of clinical factors together with FOXO1 fusion status in patients with nonmetastatic rhabdomyosarcoma (RMS) to develop a predictive model for event-free survival and provide a rationale for risk stratification in future trials. METHODS: The authors used data from patients enrolled in the European Pediatric Soft Tissue Sarcoma Study Group (EpSSG) RMS 2005 study (EpSSG RMS 2005; EudraCT number 2005-000217-35). The following baseline variables were considered for the multivariable model: age at diagnosis, sex, histology, primary tumor site, Intergroup Rhabdomyosarcoma Studies group, tumor size, nodal status, and FOXO1 fusion status. Main effects and significant second-order interactions of candidate predictors were included in a multiple Cox proportional hazards regression model. A nomogram was generated for predicting 5-year event-free survival (EFS) probabilities. RESULTS: The EFS and overall survival rates at 5 years were 70.9% (95% confidence interval, 68.6%-73.1%) and 81.0% (95% confidence interval, 78.9%-82.8%), respectively. The multivariable model retained five prognostic factors, including age at diagnosis interacting with tumor size, tumor primary site, Intergroup Rhabdomyosarcoma Studies clinical group, and FOXO1 fusion status. Based on each patient's total score in the nomogram, patients were stratified into four groups. The 5-year EFS rates were 94.1%, 78.4%, 65.2%, and 52.1% in the low-risk, intermediate-risk, high-risk, and very-high-risk groups, respectively, and the corresponding 5-year overall survival rates were 97.2%, 91.5%, 74.3%, and 60.8%, respectively. CONCLUSIONS: The results presented here provide the rationale to modify the EpSSG stratification, with the most significant change represented by the replacement of histology with fusion status. This classification was adopted in the new international trial launched by the EpSSG.

A Screening Approach to the Safe-and-Sustainable-by-Design Development of Advanced Insulation Materials.

Herein, a Safe-and-Sustainable-by-Design (SSbD) screening strategy on four different inorganic aerogel mats and two conventional mineral wools for ranking purposes is demonstrated. Given that they do not consist of particles, the release is first simulated, addressing three occupational exposure scenarios, realistic for their intended use as building insulators. No exposure to consumers nor to the environment is foreseen in the use phase, however, aerosols may be released during mat installation, posing an inhalation risk for workers. All four aerogel mats release more respirable dust than the benchmark materials and 60% thereof deposits in the alveolar region according to modelling tools. The collected aerogel dust allows for subsequent screening of hazard implications via two abiotic assays: 1) surface reactivity in human blood serum; 2) biodissolution kinetics in lung simulant fluids. Both aerogels and conventional insulators show similar surface reactivity. Differences in biodissolution are influenced by the specifically designed organic and inorganic structural modifications. Aerogel mats are better-performing insulators (2-fold lower thermal conductivity than the benchmark) However, this work demonstrates how investment decisions can be balanced with safety and sustainability aspects. Concepts of analogy and similarity thus support easily accessible methods to companies for safe and economically viable innovation with advanced materials.

Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period.

BACKGROUND: Severe heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period. METHODS: We extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values. RESULTS: A total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality. CONCLUSIONS: The survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

Potential public health hazards related to consumption of poultry contaminated with antibiotic resistant Listeria monocytogenes in Egypt.

Listeria monocytogenes is an important foodborne pathogen that incorporated into many serious infections in human especially immunocompromised individuals, pregnant women, the elderly, and newborns. The consumption of food contaminated with such bacteria is considered a source of potential risk for consumers. Therefore, a total of 250 poultry purchased in highly popular poultry stores besides 50 swabs from workers hands in the same stores, in Mansoura City had been tested for the L. monocytogenes prevalence, virulence genes, and antibiotic resistance profile illustrating the health hazards from such poultry. The L. monocytogenes were recovered from 9.6% of poultry samples while not detected from workers hand swabs. The antimicrobial susceptibility of 24 L. monocytogenes strains against 24 antibiotics of seven different classes revealed high susceptibility rates to erythromycin (79.17%), streptomycin (66.67%), gentamycin (66.67%), vancomycin (58.33%), chloramphenicol (58.33%) and cefotaxime (41.67%). The majority (79.2%) of L. monocytogenes were classified as multidrug resistant strains with high resistance to tetracyclines and ß-lactams antibiotics while 16.7% of the strains were categorized as extensively resistant ones. The iap virulence-specific determination gene had been detected in all recovered L. monocytogenes isolates while 83.33 and 70.83% of the isolates harbored hylA and actA genes. In addition, the study confirmed the capability of most L. monocytogenes isolates for biofilm formation by moderate to strong production and the quantitative risk assessment illustrated the risk of developing listeriosis as the risk value exceeded 100. The current results illustrate that poultry meat can be a source of pathogenic antibiotic resistant strains that may cause infection with limited or no treatment in immunosuppressed consumers via the food chain.

Impact of climate change on atopic dermatitis: A review by the International Eczema Council.

Atopic dermatitis (AD), the most burdensome skin condition worldwide, is influenced by climatic factors and air pollution; however, the impact of increasing climatic hazards on AD remains poorly characterized. Leveraging an existing framework for 10 climatic hazards related to greenhouse gas emissions, we identified 18 studies with evidence for an impact on AD through a systematic search. Most climatic hazards had evidence for aggravation of AD the impact ranged from direct effects like particulate matter-induced AD exacerbations from wildfires to the potential for indirect effects like drought-induced food insecurity and migration. We then created maps comparing the past, present, and future projected burden of climatic hazards to global AD prevalence data. Data are lacking, especially from those regions most likely to experience more climatic hazards. We highlight gaps important for future research: understanding the synergistic impacts of climatic hazards on AD, long-term disease activity, the differential impact on vulnerable populations, and how basic mechanisms explain population-level trends.

Non-Hodgkin lymphoma incidence, mortality, and survival trends in Brunei Darussalam from 2011 to 2020.

BACKGROUND: Non-Hodgkin lymphoma (NHL) has been identified as a significant contributor to the cancer burden. This study investigates the incidence, mortality, and survival trends of NHL cancer in Brunei Darussalam from 2011 to 2020. METHODS: This is a registry-based retrospective study using de-identified data from the Brunei Darussalam Cancer Registry on patients diagnosed with NHL from 2011 to 2020 based on the ICD-10 codes C82-86. Statistical methods include descriptive statistics, age-specific and age-standardised incidence (ASIR) and mortality rates (ASMR), and joinpoint regression for trend analysis. Survival analysis was conducted using Kaplan-Meier plots, log-rank test, and Cox Proportional Hazards regression. RESULTS: From 2011 to 2020, 330 patients were diagnosed with NHL. The majority of patients were males (51.8%) and of Malay descent (82.7%). The age group most diagnosed was 55-74 years (42.3%), with a mean age at diagnosis being 55.1 years. The ASIRs were 12.12 for males and 10.39 per 100,000 for females; ASMRs were 6.11 for males and 4.76 per 100,000 for females. Diffuse large B-cell lymphoma was the most prevalent subtype, accounting for 39.1% of cases. The overall 5-year survival rate was 61.2%, with lower rates observed in older patients and those diagnosed at distant metastasis stage. Furthermore, older age and advanced stage diagnosis significantly increased mortality risk. NHL incidence and mortality rates in Brunei Darussalam remain stable over the period of 10 years, but highlights significant disparities in gender and age. CONCLUSIONS: The findings emphasize the importance of early detection and tailored treatments, especially for high-risk groups, in managing NHL's burden. These insights underline the need for focused healthcare strategies and continued research to address NHL's challenges.

Efficient estimation for left-truncated competing risks regression for case-cohort studies.

The case-cohort study design provides a cost-effective study design for a large cohort study with competing risk outcomes. The proportional subdistribution hazards model is widely used to estimate direct covariate effects on the cumulative incidence function for competing risk data. In biomedical studies, left truncation often occurs and brings extra challenges to the analysis. Existing inverse probability weighting methods for case-cohort studies with competing risk data not only have not addressed left truncation, but also are inefficient in regression parameter estimation for fully observed covariates. We propose an augmented inverse probability-weighted estimating equation for left-truncated competing risk data to address these limitations of the current literature. We further propose a more efficient estimator when extra information from the other causes is available. The proposed estimators are consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is unbiased and leads to estimation efficiency gain in the regression parameter estimation. We analyze the Atherosclerosis Risk in Communities study data using the proposed methods.

Propensity weighting plus adjustment in proportional hazards model is not doubly robust.

Recently, it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doubly robust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, when there is truly a causal effect. We demonstrate via simulation this lack of double robustness for the semiparametric Cox model, the Weibull proportional hazards model, and a simple proportional hazards flexible parametric model, with both the latter models fit via maximum likelihood. We provide a novel proof that the combination of propensity score weighting and a proportional hazards survival model, fit either via full or partial likelihood, is consistent under the null of no causal effect of the exposure on the outcome under particular censoring mechanisms if either the propensity score or the outcome model is correctly specified and contains all confounders. Given our results suggesting that double robustness only exists under the null, we outline 2 simple alternative estimators that are doubly robust for the survival difference at a given time point (in the above sense), provided the censoring mechanism can be correctly modeled, and one doubly robust method of estimation for the full survival curve. We provide R code to use these estimators for estimation and inference in the supporting information.

A Bayesian survival treed hazards model using latent Gaussian processes.

Survival models are used to analyze time-to-event data in a variety of disciplines. Proportional hazard models provide interpretable parameter estimates, but proportional hazard assumptions are not always appropriate. Non-parametric models are more flexible but often lack a clear inferential framework. We propose a Bayesian treed hazards partition model that is both flexible and inferential. Inference is obtained through the posterior tree structure and flexibility is preserved by modeling the log-hazard function in each partition using a latent Gaussian process. An efficient reversible jump Markov chain Monte Carlo algorithm is accomplished by marginalizing the parameters in each partition element via a Laplace approximation. Consistency properties for the estimator are established. The method can be used to help determine subgroups as well as prognostic and/or predictive biomarkers in time-to-event data. The method is compared with some existing methods on simulated data and a liver cirrhosis dataset.

Absolute risk from double nested case-control designs: cause-specific proportional hazards models with and without augmented estimating equations.

We estimate relative hazards and absolute risks (or cumulative incidence or crude risk) under cause-specific proportional hazards models for competing risks from double nested case-control (DNCC) data. In the DNCC design, controls are time-matched not only to cases from the cause of primary interest, but also to cases from competing risks (the phase-two sample). Complete covariate data are available in the phase-two sample, but other cohort members only have information on survival outcomes and some covariates. Design-weighted estimators use inverse sampling probabilities computed from Samuelsen-type calculations for DNCC. To take advantage of additional information available on all cohort members, we augment the estimating equations with a term that is unbiased for zero but improves the efficiency of estimates from the cause-specific proportional hazards model. We establish the asymptotic properties of the proposed estimators, including the estimator of absolute risk, and derive consistent variance estimators. We show that augmented design-weighted estimators are more efficient than design-weighted estimators. Through simulations, we show that the proposed asymptotic methods yield nominal operating characteristics in practical sample sizes. We illustrate the methods using prostate cancer mortality data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Study of the National Cancer Institute.

Meta-research: How many diagnostic or prognostic models published in radiological journals are evaluated externally?

OBJECTIVES: Prognostic and diagnostic models must work in their intended clinical setting, proven via "external evaluation", preferably by authors uninvolved with model development. By systematic review, we determined the proportion of models published in high-impact radiological journals that are evaluated subsequently. METHODS: We hand-searched three radiological journals for multivariable diagnostic/prognostic models 2013-2015 inclusive, developed using regression. We assessed completeness of data presentation to allow subsequent external evaluation. We then searched literature to August 2022 to identify external evaluations of these index models. RESULTS: We identified 98 index studies (73 prognostic; 25 diagnostic) describing 145 models. Only 15 (15%) index studies presented an evaluation (two external). No model was updated. Only 20 (20%) studies presented a model equation. Just 7 (15%) studies developing Cox models presented a risk table, and just 4 (9%) presented the baseline hazard. Two (4%) studies developing non-Cox models presented the intercept. Just 20 (20%) articles presented a Kaplan-Meier curve of the final model. The 98 index studies attracted 4224 citations (including 559 self-citations), median 28 per study. We identified just six (6%) subsequent external evaluations of an index model, five of which were external evaluations by researchers uninvolved with model development, and from a different institution. CONCLUSIONS: Very few prognostic or diagnostic models published in radiological literature are evaluated externally, suggesting wasted research effort and resources. Authors' published models should present data sufficient to allow external evaluation by others. To achieve clinical utility, researchers should concentrate on model evaluation and updating rather than continual redevelopment. CLINICAL RELEVANCE STATEMENT: The large majority of prognostic and diagnostic models published in high-impact radiological journals are never evaluated. It would be more efficient for researchers to evaluate existing models rather than practice continual redevelopment. KEY POINTS: • Systematic review of highly cited radiological literature identified few diagnostic or prognostic models that were evaluated subsequently by researchers uninvolved with the original model. • Published radiological models frequently omit important information necessary for others to perform an external evaluation: Only 20% of studies presented a model equation or nomogram. • A large proportion of research citing published models focuses on redevelopment and ignores evaluation and updating, which would be a more efficient use of research resources.

Simultaneous variable selection and estimation for survival data via the Gaussian seamless- L 0 $$ {L}_0 $$ penalty.

We propose a new simultaneous variable selection and estimation procedure with the Gaussian seamless- L 0 $$ {L}_0 $$ (GSELO) penalty for Cox proportional hazard model and additive hazards model. The GSELO procedure shows good potential to improve the existing variable selection methods by taking strength from both best subset selection (BSS) and regularization. In addition, we develop an iterative algorithm to implement the proposed procedure in a computationally efficient way. Theoretically, we establish the convergence properties of the algorithm and asymptotic theoretical properties of the proposed procedure. Since parameter tuning is crucial to the performance of the GSELO procedure, we also propose an extended Bayesian information criteria (EBIC) parameter selector for the GSELO procedure. Simulated and real data studies have demonstrated the prediction performance and effectiveness of the proposed method over several state-of-the-art methods.