Pan-Cancer Analysis of GALNT6 with Potential Implications for Prognosis and Tumor Microenvironment in Human Cancer Based on Bioinformatics and qPCR Verification.

Purpose: We explored the expression and prognostic value of GALNT6 and the tumor microenvironment of pan-cancer in humans. Methods: In this study, we explored the expression pattern of GALNT6 pan-cancer across multiple databases. The prognostic value of GALNT6 was evaluated using the Kaplan-Meier method. The types and numbers of GALNT6 gene alterations were exhibited using the cBio Cancer Genomics Portal. The correlations between GALNT6 expression and immune infiltration in cancers were analyzed using the database Tumor Immune Estimation Resource 2. We also used the Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analysis to investigate the molecular mechanisms of the GALNT6 gene in tumorigenesis. The expression of GALNT6 was also further verified by qPCR in lung adenocarcinoma tissues. Results: In general, compared with normal tissue, tumor tissue had a higher expression level of GALNT6. GALNT6 showed a protective effect in colon carcinoma and other cancers; however, a high expression level of GALNT6 was detrimental to survival in bladder cancer and in pheochromocytoma and paraganglioma. Mutation, amplification, and deep deletion were the three main types of GALNT6 mutations in tumors. There was a significant positive correlation between GALNT6 expression and immune infiltration of CD8+ T-cells in skin cutaneous melanoma metastasis, based on most of the algorithms used. Moreover, protein processing- and glycoprotein metabolic-associated functions were involved in the functional mechanisms of GALNT6. Conclusion: This first pan-cancer study offers a relatively comprehensive understanding of the oncogenic roles of GALNT6 across different cancer types.

Effectiveness of training parturition and dystocia management on days open of dairy cows in traditional farming systems: a field trial.

The interval between parturition and subsequent pregnancy is called the days open or calving to conception interval and is affected by several factors, especially dystocia. Dystocia is an ab-normal or difficult calving that may require assistance during labor. This study is a field trial in health education and the research team developed a comprehensive training program for farmers to educate them about the normal process of parturition in dairy cows and when and how to assist in parturition or dystocia. A series of classes was held for farmers and the study covered 486 multi-parous dairy cows, with 173 belonging to the group of trained farms (educated farmers) and 313 to the control group (non-educated farmers). Although dystocia was lower in the educated group, there were no significant differences in retained placenta between two groups. However, cows in the educated group had a better conception rate (lower service per conception) in sub-sequent parturitions. Hence, the median number of days open for cows from trained farmers was significantly lower than other farmers (85 days compared to 120 days, respectively). Based on Cox regression analysis, uterine prolapse, retained placenta, and dystocia could significantly impact subsequent pregnancies. Dystocia affects days open, and training on parturition and dystocia management can effectively reduce the numbers of days open in dairy cows.

A method for determining groups in cumulative incidence curves in competing risk data.

The cumulative incidence function is the standard method for estimating the marginal probability of a given event in the presence of competing risks. One basic but important goal in the analysis of competing risk data is the comparison of these curves, for which limited literature exists. We proposed a new procedure that lets us not only test the equality of these curves but also group them if they are not equal. The proposed method allows determining the composition of the groups as well as an automatic selection of their number. Simulation studies show the good numerical behavior of the proposed methods for finite sample size. The applicability of the proposed method is illustrated using real data.

Estimating and Presenting Hazard Ratios and Absolute Risks from a Cox Model with Complex Non-linear Interactions.

Interaction analysis is a critical component of clinical and public health research and represents a key topic in precision health and medicine. In applied settings, however, interaction assessment is usually limited to the test of a product term in a regression model, and to the presentation of stratified results over levels of additional covariates. Results stratification often relies on categorizing or making linearity assumptions for continuous covariates, with substantial loss of precision and of relevant information. In time-to-event analysis, moreover, interaction assessment is often limited to the multiplicative hazard scale by inclusion of a product terms in a Cox regression model, disregarding the clinically relevant information that are captured by the absolute risk scale. In this paper we present a user-friendly procedure, based on the prediction of individual absolute risks from the Cox model, for the estimation and presentation of interactive effects on both the multiplicative and additive scale in survival analysis. We describe how to flexibly incorporate interactions with continuous covariates, which potentially operate in a non-linear fashion, we provide software material to replicate our procedure, and discuss different approaches to derive confidence intervals. The presented approach will allow clinical and public health researchers assessing complex relationships between multiple covariates as they relate to a clinical endpoint, and providing a more intuitive and precise depiction of the results in applied research papers focusing on interaction and effect stratification.

Serum Albumin as an Independent Predictor of Long-Term Survival in Patients with Recurrent and Metastatic Head and Neck Squamous Cell Carcinoma Treated with Nivolumab.

Background: Nivolumab has been shown to improve the overall survival (OS) of patients with recurrent and metastatic head and neck squamous cell carcinoma (R/M HNSCC). However, there is a need to identify factors associated with long-term survival (beyond 2 years) in these patients. This study investigated the relationship between pretreatment factors and long-term survival in patients with R/M HNSCC treated with nivolumab. Methods: Forty-nine patients with R/M HNSCC who were treated with nivolumab were retrospectively reviewed. Baseline characteristics, clinical data, and survival outcomes were evaluated. Univariate and multivariate analyses were performed to identify factors associated with long-term survival (OS ≥ 2 years). Results: The median OS in the overall cohort was 11.0 months, and the 2-year survival rate was 34.7%. Long-term survivors (OS ≥ 2 years) had significantly higher proportions of patients with Eastern Cooperative Oncology Group (ECOG) performance status (PS) scores of 0 or 1, serum albumin levels ≥ 3.5 g/dL, and neutrophil-to-eosinophil ratio (NER) < 32.0 compared to non-long-term survivors. On multivariate analysis, serum albumin levels ≥ 3.5 g/dL, in addition to ECOG-PS score of 0 or 1, were independent predictors of long-term survival. Conclusions: Pretreatment serum albumin levels may be useful for predicting long-term survival in R/M HNSCC patients treated with nivolumab.

Diverticulitis Is Associated with Increased Risk of Colon Cancer-A Nationwide Register-Based Cohort Study.

Background: An association between diverticulitis and colon cancer has been proposed. The evidence is conflicting, and the guidelines differ regarding recommended follow-up with colonoscopy after an episode of diverticulitis. To guide regimes for follow-up, this study aimed to investigate if patients with diverticulitis have an increased risk of colon cancer. Methods: This study is reported according to the RECORD statement. We performed a cohort study with linked data from nationwide Danish registers. The inclusion period was 1997-2009, and the complete study period was 1995-2013. The primary outcome was the risk of developing colon cancer estimated using a Cox regression analysis with time-varying covariates. We performed a sensitivity analysis on a cohort of people with prior colonoscopies, comparing the risk of colon cancer between the diverticulitis group and the control group. Results: We included 29,173 adult males and females with diverticulitis and 145,865 controls matched for sex and age. The incidence proportion of colon cancer was 2.1% (95% confidence interval (CI) 1.9-2.3) in the diverticulitis group and 1.5% (95% CI 1.4-1.5) in the matched control group (hazard ratio 1.6; 95% CI 1.5-1.8). The risk of having a colon cancer diagnosis was significantly increased in the first six months after inclusion (hazard ratio 1.7; 95% CI 1.5-1.8), and hereafter there was a lower risk in the diverticulitis group compared with controls (hazard ratio 0.8; 95% CI 0.7-0.9). This protective effect lasted eight years. The increased risk of colon cancer during the first six months after diverticulitis was also found in the cohort with prior colonoscopies. Conclusions: The risk of a colon cancer diagnosis was significantly increased for patients with diverticulitis 0-6 months after the diagnosis of diverticulitis. Hereafter, we found a protective effect of diverticulitis until eight years later, possibly due to a screening effect. We recommend a follow-up colonoscopy after the first diagnosis of diverticulitis.

A Sensory Shelf-Life Study for the Evaluation of New Eco-Sustainable Packaging of Single-Portion Croissants.

Understanding the correlation between straightforward analytical methods and sensory attributes is pivotal for transitioning to sustainable packaging while improving product quality. In this context, the viability of eco-sustainable packaging alternatives for single-packaged croissants has been investigated through examining the correlations between analytical methods, sensory attributes, employing quantitative descriptive analysis (QDA), and consumer survival analysis. The performance of biaxially oriented polypropylene (BOPP), a petrochemical plastic film, against paper-based, compostable, and biodegradable films over a 150-day croissant storage period was compared in this study, examining both physiochemical and sensory perspectives. The results showed a correlation between a lower water vapour barrier in packaging materials and increased moisture migration and croissant hardness, as assessed by the Avrami kinetic model. Notably, given its reduced barrier properties, the compostable film accelerated sensory profile deterioration, as evidenced by QDA results. Shelf-life estimation, assessed by consumer rejection, underscored the viability of the biodegradable film for up to 185 days, surpassing BOPP, paper-based, and other biodegradable alternatives. Using linear regression, physiochemical parameters associated with predicted shelf-life were elucidated. Overall, croissants were rejected by 50% of consumers when they reached humidity levels below 18%, water activity below 0.81, firmness exceeding 1064 N, pH above 4.4, and acidity below 4.5. Based on the results of this study, biodegradable packaging emerges as a promising alternative to traditional BOPP, offering a sustainable opportunity to extend the shelf-life of croissants.

WhARIO: whole-slide-image-based survival analysis for patients treated with immunotherapy.

Purpose: Immune checkpoint inhibitors (ICIs) are now one of the standards of care for patients with lung cancer and have greatly improved both progression-free and overall survival, although <20% of the patients respond to the treatment, and some face acute adverse events. Although a few predictive biomarkers have integrated the clinical workflow, they require additional modalities on top of whole-slide images and lack efficiency or robustness. In this work, we propose a biomarker of immunotherapy outcome derived solely from the analysis of histology slides. Approach: We develop a three-step framework, combining contrastive learning and nonparametric clustering to distinguish tissue patterns within the slides, before exploiting the adjacencies of previously defined regions to derive features and train a proportional hazards model for survival analysis. We test our approach on an in-house dataset of 193 patients from 5 medical centers and compare it with the gold standard tumor proportion score (TPS) biomarker. Results: On a fivefold cross-validation (CV) of the entire dataset, the whole-slide image-based survival analysis for patients treated with immunotherapy (WhARIO) features are able to separate a low- and a high-risk group of patients with a hazard ratio (HR) of 2.29 (CI95=1.48 to 3.56), whereas the TPS 1% reference threshold only reaches a HR of 1.81 (CI95=1.21 to 2.69). Combining the two yields a higher HR of 2.60 (CI95=1.72 to 3.94). Additional experiments on the same dataset, where one out of five centers is excluded from the CV and used as a test set, confirm these trends. Conclusions: Our uniquely designed WhARIO features are an efficient predictor of survival for lung cancer patients who received ICI treatment. We achieve similar performance to the current gold standard biomarker, without the need to access other imaging modalities, and show that both can be used together to reach even better results.

Identification of glutamine metabolism-related gene signature to predict colorectal cancer prognosis.

Backgrounds: Colorectal cancer (CRC) is a highly malignant gastrointestinal malignancy with a poor prognosis, which imposes a significant burden on patients and healthcare providers globally. Previous studies have established that genes related to glutamine metabolism play a crucial role in the development of CRC. However, no studies have yet explored the prognostic significance of these genes in CRC. Methods: CRC patient data were downloaded from The Cancer Genome Atlas (TCGA), while glutamine metabolism-related genes were obtained from the Molecular Signatures Database (MSigDB) database. Univariate COX regression analysis and LASSO Cox regression were utilized to identify 15 glutamine metabolism-related genes associated with CRC prognosis. The risk scores were calculated and stratified into high-risk and low-risk groups based on the median risk score. The model's efficacy was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis. Cox regression analysis was employed to determine the risk score as an independent prognostic factor for CRC. Differential immune cell infiltration between the high-risk and low-risk groups was assessed using the ssGSEA method. The clinical applicability of the model was validated by constructing nomograms based on age, gender, clinical staging, and risk scores. Immunohistochemistry (IHC) was used to detect the expression levels of core genes. Results: We identified 15 genes related to glutamine metabolism in CRC: NLGN1, RIMKLB, UCN, CALB1, SYT4, WNT3A, NRCAM, LRFN4, PHGDH, GRM1, CBLN1, NRG1, GLYATL1, CBLN2, and VWC2. Compared to the high-risk group, the low-risk group demonstrated longer overall survival (OS) for CRC. Clinical correlation analysis revealed a positive correlation between the risk score and the clinical stage and TNM stage of CRC. Immune correlation analysis indicated a predominance of Th2 cells in the low-risk group. The nomogram exhibited excellent discriminatory ability for OS in CRC. Immunohistochemistry revealed that the core gene CBLN1 was expressed at a lower level in CRC, while GLYATL1 was expressed at a higher level. Conclusions: In summary, we have successfully identified and comprehensively analyzed a gene signature associated with glutamine metabolism in CRC for the first time. This gene signature consistently and reliably predicts the prognosis of CRC patients, indicating its potential as a metabolic target for individuals with CRC.

Life, death, and statins: association of statin prescriptions and survival in older general practice patients.

AIMS: This study serves as an exemplar to demonstrate the scalability of a research approach using survival analysis applied to general practice electronic health record data from multiple sites. Collection of these data, the subsequent analysis, and the preparation of practice-specific reports were performed using a bespoke distributed data collection and analysis software tool. BACKGROUND: Statins are a very commonly prescribed medication, yet there is a paucity of evidence for their benefits in older patients. We examine the relationship between statin prescriptions for general practice patients over 75 and all-cause mortality. METHODS: We carried out a retrospective cohort study using survival analysis applied to data extracted from the electronic health records of five Australian general practices. FINDINGS: The data from 8025 patients were analysed. The median duration of follow-up was 6.48 years. Overall, 52 015 patient-years of data were examined, and the outcome of death from any cause was measured in 1657 patients (21%), with the remainder being censored. Adjusted all-cause mortality was similar for participants not prescribed statins versus those who were (HR 1.05, 95% CI 0.92-1.20, P = 0.46), except for patients with diabetes for whom all-cause mortality was increased (HR = 1.29, 95% CI: 1.00-1.68, P = 0.05). In contrast, adjusted all-cause mortality was significantly lower for patients deprescribed statins compared to those who were prescribed statins (HR 0.81, 95% CI 0.70-0.93, P < 0.001), including among females (HR = 0.75, 95% CI: 0.61-0.91, P < 0.001) and participants treated for secondary prevention (HR = 0.72, 95% CI: 0.60-0.86, P < 0.001). This study demonstrated the scalability of a research approach using survival analysis applied to general practice electronic health record data from multiple sites. We found no evidence of increased mortality due to statin-deprescribing decisions in primary care.

A comparison of computational algorithms for the Bayesian analysis of clinical trials.

BACKGROUND: Clinical trials are increasingly using Bayesian methods for their design and analysis. Inference in Bayesian trials typically uses simulation-based approaches such as Markov Chain Monte Carlo methods. Markov Chain Monte Carlo has high computational cost and can be complex to implement. The Integrated Nested Laplace Approximations algorithm provides approximate Bayesian inference without the need for computationally complex simulations, making it more efficient than Markov Chain Monte Carlo. The practical properties of Integrated Nested Laplace Approximations compared to Markov Chain Monte Carlo have not been considered for clinical trials. Using data from a published clinical trial, we aim to investigate whether Integrated Nested Laplace Approximations is a feasible and accurate alternative to Markov Chain Monte Carlo and provide practical guidance for trialists interested in Bayesian trial design. METHODS: Data from an international Bayesian multi-platform adaptive trial that compared therapeutic-dose anticoagulation with heparin to usual care in non-critically ill patients hospitalized for COVID-19 were used to fit Bayesian hierarchical generalized mixed models. Integrated Nested Laplace Approximations was compared to two Markov Chain Monte Carlo algorithms, implemented in the software JAGS and stan, using packages available in the statistical software R. Seven outcomes were analysed: organ-support free days (an ordinal outcome), five binary outcomes related to survival and length of hospital stay, and a time-to-event outcome. The posterior distributions for the treatment and sex effects and the variances for the hierarchical effects of age, site and time period were obtained. We summarized these posteriors by calculating the mean, standard deviations and the 95% equitailed credible intervals and presenting the results graphically. The computation time for each algorithm was recorded. RESULTS: The average overlap of the 95% credible interval for the treatment and sex effects estimated using Integrated Nested Laplace Approximations was 96% and 97.6% compared with stan, respectively. The graphical posterior densities for these effects overlapped for all three algorithms. The posterior mean for the variance of the hierarchical effects of age, site and time estimated using Integrated Nested Laplace Approximations are within the 95% credible interval estimated using Markov Chain Monte Carlo but the average overlap of the credible interval is lower, 77%, 85.6% and 91.3%, respectively, for Integrated Nested Laplace Approximations compared to stan. Integrated Nested Laplace Approximations and stan were easily implemented in clear, well-established packages in R, while JAGS required the direct specification of the model. Integrated Nested Laplace Approximations was between 85 and 269 times faster than stan and 26 and 1852 times faster than JAGS. CONCLUSION: Integrated Nested Laplace Approximations could reduce the computational complexity of Bayesian analysis in clinical trials as it is easy to implement in R, substantially faster than Markov Chain Monte Carlo methods implemented in JAGS and stan, and provides near identical approximations to the posterior distributions for the treatment effect. Integrated Nested Laplace Approximations was less accurate when estimating the posterior distribution for the variance of hierarchical effects, particularly for the proportional odds model, and future work should determine if the Integrated Nested Laplace Approximations algorithm can be adjusted to improve this estimation.

NT-proBNP point-of-care testing for predicting mortality in end-stage renal disease: A survival analysis.

This study examines the predictive value of elevated N-terminal-pro brain natriuretic peptide (NT-pro BNP) levels for mortality among patients with end-stage renal disease (ESRD). Data from 768 ESRD patients, excluding those with cancer or lost follow-up, were analyzed using Kaplan-Meier curves and Cox proportional hazards models over three years. Results indicated that patients with very high NT-pro BNP levels had shorter average survival times and a significantly higher risk of mortality (hazard ratio 1.43). Advanced age, ICU admission, and comorbidities like cerebrovascular diseases and chronic obstructive pulmonary disease also contributed to increased mortality risks. Thus, elevated NT-pro BNP is an independent risk factor for mortality in ESRD patients.

Direct and indirect treatment effects in the presence of semicompeting risks.

Semicompeting risks refer to the phenomenon that the terminal event (such as death) can censor the nonterminal event (such as disease progression) but not vice versa. The treatment effect on the terminal event can be delivered either directly following the treatment or indirectly through the nonterminal event. We consider 2 strategies to decompose the total effect into a direct effect and an indirect effect under the framework of mediation analysis in completely randomized experiments by adjusting the prevalence and hazard of nonterminal events, respectively. They require slightly different assumptions on cross-world quantities to achieve identifiability. We establish asymptotic properties for the estimated counterfactual cumulative incidences and decomposed treatment effects. We illustrate the subtle difference between these 2 decompositions through simulation studies and two real-data applications in the Supplementary Materials.

The association between referral by specialists in oral diagnosis on survival rates of patients with oral cancer: A retrospective cohort study.

BACKGROUND: To assess the influence of diagnosis and referral provided by specialists in oral diagnosis on disease-free survival and overall survival of patients with oral cancer. METHODS: A cohort of 282 patients with oral cancer treated at a regional cancer hospital from 1998 to 2016 was analyzed retrospectively. The referral register of the patients was analyzed and assigned to two groups: (1) those referred by oral diagnosis specialists (n = 129), or (2) those referred by nonspecialized professionals (n = 153). The cancer treatment evolution was assessed from the patients' records, and the outcome was registered concerning cancer recurrence and death. Sociodemographic and clinicopathological variables were explored as predictors of disease-free survival and overall survival. RESULTS: Group 1 exhibited lower T stages and a reduced incidence of regional and distant metastases. Surgery was performed in 75.2% of cases in Group 1, while in Group 2, the rate was 60.8%. Advanced T stages and regional metastases reduced the feasibility of surgery. Higher TNM stages and tumor recurrence were associated with decreased disease-free survival, while surgical intervention was a protective factor. Higher TNM stage had a negative impact on the overall survival. CONCLUSION: Specialized oral diagnosis did not directly impact disease-free survival and overall survival and did not influence the indication of surgery in oral cancer; however, it was associated with the diagnosis of early tumors and better prognosis.

Wait times and breast cancer survival: a population-based retrospective cohort study using CanIMPACT data.

PURPOSE: The time from breast cancer surgery to chemotherapy has been shown to affect survival outcomes; however, the effect of time from first breast cancer-related healthcare contact to first cancer specialist consultation, or the time from first breast cancer-related healthcare contact to adjuvant chemotherapy on survival has not been well explored. We aimed to determine whether various wait times along the breast cancer treatment pathway (contact-to-consultation, contact-to-chemotherapy, surgery-to-chemotherapy) were associated with overall survival in women within the Canadian province of Ontario. METHODS: We performed a population-based retrospective cohort study of women diagnosed with stage I-III breast cancer in Ontario between 2007 and 2011 who received surgery and adjuvant chemotherapy. This was the Ontario cohort of a larger, nationwide study (the Canadian Team to improve Community-Based Cancer Care along the Continuum - CanIMPACT). We used Cox-proportional hazards regression to determine the association between the contact-to-consultation, contact-to-chemotherapy, and surgery-to-chemotherapy intervals and overall survival while adjusting for cancer stage, age, comorbidity, neighborhood income, immigration status, surgery type, and method of cancer detection. RESULTS: Among 12,782 breast cancer patients, longer surgery-to-chemotherapy intervals (HR 1.13, 95% CI 1.03-1.18 per 30-day increase), but not the contact-to-consultation (HR 0.979, 95% CI 0.95-1.01 per 30-day increase), nor the more comprehensive contact-to-chemotherapy intervals (HR 1.00, 95% CI 0.98-1.02 per 30-day increase) were associated with decreased survival in our adjusted analyses. CONCLUSION: Our findings emphasize the prognostic importance of a shorter surgery-to-chemotherapy interval, whereas the contact-to-consultation and contact-to-chemotherapy intervals have less impact on survival outcomes.

Differential analysis of histopathological and genetic markers of cancer aggressiveness, and survival difference in EBV-positive and EBV-negative prostate carcinoma.

Several studies have shown an association between prostate carcinoma (PCa) and Epstein-Barr virus (EBV); however, none of the studies so far have identified the histopathological and genetic markers of cancer aggressiveness associated with EBV in PCa tissues. In this study, we used previously characterized EBV-PCR-positive (n = 39) and EBV-negative (n = 60) PCa tissues to perform an IHC-based assessment of key histopathological and molecular markers of PCa aggressiveness (EMT markers, AR expression, perineural invasion, and lymphocytic infiltration characterization). Additionally, we investigated the differential expression of key oncogenes, EMT-associated genes, and PCa-specific oncomiRs, in EBV-positive and -negative tissues, using the qPCR array. Finally, survival benefit analysis was also performed in EBV-positive and EBV-negative PCa patients. The EBV-positive PCa exhibited a higher percentage (80%) of perineural invasion (PNI) compared to EBV-negative PCa (67.3%) samples. Similarly, a higher lymphocytic infiltration was observed in EBV-LMP1-positive PCa samples. The subset characterization of T and B cell lymphocytic infiltration showed a trend of higher intratumoral and tumor stromal lymphocytic infiltration in EBV-negative tissues compared with EBV-positive tissues. The logistic regression analysis showed that EBV-positive status was associated with decreased odds (OR = 0.07; p-value < 0.019) of CD3 intratumoral lymphocytic infiltration in PCa tissues. The analysis of IHC-based expression patterns of EMT markers showed comparable expression of all EMT markers, except vimentin, which showed higher expression in EBV-positive PCa tissues compared to EBV-negative PCa tissues. Furthermore, gene expression analysis showed a statistically significant difference (p < 0.05) in the expression of CDH1, AR, CHEK-2, CDKN-1B, and CDC-20 and oncomiRs miR-126, miR-152-3p, miR-452, miR-145-3p, miR-196a, miR-183-3p, and miR-146b in EBV-positive PCa tissues compared to EBV-negative PCa tissues. Overall, the survival proportion was comparable in both groups. The presence of EBV in the PCa tissues results in an increased expression of certain oncogenes, oncomiRs, and EMT marker (vimentin) and a decrease in CD3 ITL, which may be associated with the aggressive forms of PCa.

Massive Parallelization of Massive Sample-size Survival Analysis.

Large-scale observational health databases are increasingly popular for conducting comparative effectiveness and safety studies of medical products. However, increasing number of patients poses computational challenges when fitting survival regression models in such studies. In this paper, we use graphics processing units (GPUs) to parallelize the computational bottlenecks of massive sample-size survival analyses. Specifically, we develop and apply time- and memory-efficient single-pass parallel scan algorithms for Cox proportional hazards models and forward-backward parallel scan algorithms for Fine-Gray models for analysis with and without a competing risk using a cyclic coordinate descent optimization approach. We demonstrate that GPUs accelerate the computation of fitting these complex models in large databases by orders of magnitude as compared to traditional multi-core CPU parallelism. Our implementation enables efficient large-scale observational studies involving millions of patients and thousands of patient characteristics. The above implementation is available in the open-source R package Cyclops (Suchard et al., 2013).

Survival analysis of freezing stress in the North American native perennial flax, Linum lewisii.

Abstract. The expansive range of Lewis flax (Linum lewisii), an herbaceous perennial, exposes the species to a diversity of climatic conditions. As interest in the domestication and adoption of perennial crop alternatives grows and interest in this species for natural area restoration continues, the assurance of a commercial plant variety's ability to endure the full range of possible climatic extremes is paramount. This study examines the freezing tolerance of a geographically representative sampling of 44 Lewis flax accessions at winter temperature extremes experienced in the northern Great Plains of the USA. Survival analysis models were adapted to include temperature exposure, in replacement of ordinal time typically used in such models, to produce statistics evaluating reactions to extreme temperatures that Lewis flax would encounter in our field environments. Our results revealed Lewis flax is more freezing tolerant than previously reported, and revealed four accessions with significantly superior genetic freezing tolerance than the released 'Maple Grove' cultivar. Furthermore, regrowth analyses indicate variation among accessions not associated with survival, which could lead to improving regrowth rate and survival simultaneously. These findings and their methodology expand the understanding of Lewis flax adaptation for winter hardiness and offer an efficient, new model that can be used to evaluate freezing tolerance at ordinal temperatures without requiring extensive prior physiological knowledge for a species.

Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network.

In recent times, time-to-event data such as time to failure or death is routinely collected alongside high-throughput covariates. These high-dimensional bioinformatics data often challenge classical survival models, which are either infeasible to fit or produce low prediction accuracy due to overfitting. To address this issue, the focus has shifted towards introducing a novel approaches for feature selection and survival prediction. In this article, we propose a new hybrid feature selection approach that handles high-dimensional bioinformatics datasets for improved survival prediction. This study explores the efficacy of four distinct variable selection techniques: LASSO, RSF-vs, SCAD, and CoxBoost, in the context of non-parametric biomedical survival prediction. Leveraging these methods, we conducted comprehensive variable selection processes. Subsequently, survival analysis models-specifically CoxPH, RSF, and DeepHit NN-were employed to construct predictive models based on the selected variables. Furthermore, we introduce a novel approach wherein only variables consistently selected by a majority of the aforementioned feature selection techniques are considered. This innovative strategy, referred to as the proposed method, aims to enhance the reliability and robustness of variable selection, subsequently improving the predictive performance of the survival analysis models. To evaluate the effectiveness of the proposed method, we compare the performance of the proposed approach with the existing LASSO, RSF-vs, SCAD, and CoxBoost techniques using various performance metrics including integrated brier score (IBS), concordance index (C-Index) and integrated absolute error (IAE) for numerous high-dimensional survival datasets. The real data applications reveal that the proposed method outperforms the competing methods in terms of survival prediction accuracy.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features.

BACKGROUND: Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients' survival analysis. METHODS: Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. RESULTS: Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. CONCLUSIONS: The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. TRIAL REGISTRATION: ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.