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OBJECTIVE: To investigate Covid-19 vaccination as a potential secondary public health benefit of case management for Medicaid beneficiaries with health and social needs. DATA SOURCES AND STUDY SETTING: The CommunityConnect case management program for Medicaid beneficiaries is run by Contra Costa Health, a county safety net health system in California. Program enrollment data were merged with comprehensive county vaccination records. STUDY DESIGN: Individuals with elevated risk of hospital and emergency department use were randomized each month to a case management intervention or usual care. Interdisciplinary case managers offered coaching, community referrals, healthcare connections, and other support based on enrollee interest and need. Using survival analysis with intent-to-treat assignment, we assessed rates of first-dose Covid-19 vaccination from December 2020 to September 2021. In exploratory sub-analyses we also examined effect heterogeneity by gender, race/ethnicity, age, and primary language. DATA COLLECTION AND EXTRACTION METHODS: Data were extracted from county and program records as of September 2021, totaling 12,866 interventions and 25,761 control enrollments. PRINCIPAL FINDINGS: Approximately 58% of enrollees were female and 41% were under age 35. Enrollees were 23% White, 12% Asian/Pacific Islander, 20% Black/African American, and 36% Hispanic/Latino, and 10% other/unknown. Approximately 35% of the intervention group engaged with their case manager. Approximately 56% of all intervention and control enrollees were vaccinated after 9 months of analysis time. Intervention enrollees had a higher vaccination rate compared to control enrollees (adjusted hazard ratio [aHR]: 1.06; 95% confidence interval [CI]: 1.02-1.10). In sub-analyses, the intervention was associated with stronger likelihood of vaccination among males and individuals under age 35. CONCLUSIONS: Case management infrastructure modestly improved Covid-19 vaccine uptake in a population of Medicaid beneficiaries that over-represents social groups with barriers to early Covid-19 vaccination. Amidst mixed evidence on vaccination-specific incentives, leveraging trusted case managers and existing case management programs may be a valuable prevention strategy.

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

We explored the dysregulation of G-protein-coupled receptor (GPCR) ligand systems in cancer transcriptomics datasets to uncover new therapeutics opportunities in oncology. We derived an interaction network of receptors with ligands and their biosynthetic enzymes. Multiple GPCRs are differentially regulated together with their upstream partners across cancer subtypes and are associated to specific transcriptional programs and to patient survival patterns. The expression of both receptor-ligand (or enzymes) partners improved patient stratification, suggesting a synergistic role for the activation of GPCR networks in modulating cancer phenotypes. Remarkably, we identified many such axes across several cancer molecular subtypes, including many involving receptor-biosynthetic enzymes for neurotransmitters. We found that GPCRs from these actionable axes, including, e.g., muscarinic, adenosine, 5-hydroxytryptamine, and chemokine receptors, are the targets of multiple drugs displaying anti-growth effects in large-scale, cancer cell drug screens, which we further validated. We have made the results generated in this study freely available through a webapp (gpcrcanceraxes.bioinfolab.sns.it).

This study explores the relationship between the governance of architecturally embedded applications and their longevity in organizational use. Using a contingency-fit logic, it posits that alignment between application governance (distribution of decision rights) and architectural contingencies contributes to sustained durations of organizational use. Architectural contingencies include internal application modularity, application inter-connectedness, and governance of surrounding application clusters. Evidence for the governance-architecture fit hypothesis is derived from analyzing temporally ordered data about applications (n = 225, n = 498, n = 193) from organizations in the media, banking, and utilities sectors over five years. Using a two-step approach with ordinal regression, along with Cox proportional hazards and logistic regression models, our findings indicate that a fit between observed governance and the governance induced by architectural contingencies reduces the likelihood of application decommissioning. The study advances the fields of information systems (IS) governance and architecture by offering a micro-level perspective on the longevity of organizational IS use while also shedding light on the importance of governance choices aligned with unique architectural application properties. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Salivary duct carcinoma (SDC) is a rare and aggressive salivary gland malignancy. Herein, we present the largest single-institution review of SDC to date. METHODS: This is a retrospective cohort study of all histologically confirmed cases of SDC seen at our institution from January 1, 2002, to August 1, 2022. Patient demographics, treatment, histological characteristics, tumor staging, and outcomes were extracted from the electronic medical record. Kaplan-Meier and Cox regression survival analyses were performed. RESULTS: This study included 119 patients with a mean age of 66.2 years. Most primary tumors arose from the parotid gland (72.3%), and 23.5% were noted to be carcinoma ex-pleomorphic adenoma. 57.1% of patients presented with regional lymph node metastasis, whereas 23.5% presented with distant disease. Kaplan-Meier analysis demonstrated a 62.4% 5-year overall survival (OS) and a 69.0% 5-year disease-specific survival (DSS). Univariate analyses indicated that presence of regional lymph node disease (p

There is a need to reconsider the acceptance of organs from donors considered suboptimal, in the absence of data. Toxoplasma antibody-positive donors (TPD) constitute one such group. The objective of our study was to compare graft survival in deceased donor renal transplant (Tx) recipients, stratified by Toxoplasma IgG status, using the Organ Procurement and Transplantation Network (OPTN) database. A log-linear event history regression model for graft failure categorized by Toxoplasma IgG status, adjusting for confounders was applied to first kidney-only Tx recipients from 2018 to 2022. Of the 51,422 Tx, 4,317 (8.4%) were from TPD. Acute rejection and graft failure (5% each) were similar between groups. Crude graft failure was 7.3 failures per 100 person-years for TPD recipients compared to 6.5 failures per 100 person-years for the Toxoplasma-negative group (p 0.008). The crude failure rate ratio was 1.14 with an adjusted hazard rate ratio of 1.04 (95% CI: 0.94, 1.15, p 0.39). In renal Tx recipients, TPD graft recipients have comparable survival to Tx from Toxoplasma-negative recipients. While caution and close monitoring of recipients post-Tx for surveillance of disseminated toxoplasmosis are still warranted, our study suggests that patients can be successfully managed using TPD organs.

INTRODUCTION: Non-Hispanic Black (NHB) Americans have a higher incidence of colorectal cancer (CRC) and worse survival than non-Hispanic white (NHW) Americans, but the relative contributions of biological versus access to care remain poorly characterized. This study used two nationwide cohorts in different healthcare contexts to study health system effects on this disparity. METHODS: We used data from the Surveillance, Epidemiology, and End Results (SEER) registry as well as the United States Veterans Health Administration (VA) to identify adults diagnosed with colorectal cancer between 2010 and 2020 who identified as non-Hispanic Black (NHB) or non-Hispanic white (NHW). Stratified survival analyses were performed using a primary endpoint of overall survival, and sensitivity analyses were performed using cancer-specific survival. RESULTS: We identified 263,893 CRC patients in the SEER registry (36,662 (14%) NHB; 226,271 (86%) NHW) and 24,375 VA patients (4,860 (20%) NHB; 19,515 (80%) NHW). In the SEER registry, NHB patients had worse OS than NHW patients: median OS of 57 months (95% confidence interval (CI) 55-58) versus 72 months (95% CI 71-73) (hazard ratio (HR) 1.14, 95% CI 1.12-1.15, p = 0.001). In contrast, VA NHB median OS was 65 months (95% CI 62-69) versus NHW 69 months (95% CI 97-71) (HR 1.02, 95% CI 0.98-1.07, p = 0.375). There was significant interaction in the SEER registry between race and Medicare age eligibility (p

Accurate prediction of remaining useful life (RUL) for in‐service systems plays an important role in ensuring efficient operation of industrial equipment and in preventing unexpected equipment failures. In this paper, we present a prognostic framework for real‐time RUL prediction based on joint modeling of both degradation signals and time‐to‐event data. The proposed model employs a change point‐based general path model to capture signal non‐linearity and Neural network (NN) based Cox model to link the time‐to‐event data with the estimated degradation trend. An empirical two‐step scheme for hyperparameter estimation is proposed to enhance prognostic accuracy. Furthermore, an efficient Bayesian model updating procedure, integrated with recursive particle filtering, is used to facilitate online prediction, achieving accurate RUL prediction in real‐time and accounting for uncertainties in RUL prediction. Simulation and real‐life case studies demonstrate the advantages of the proposed method over existing approaches. [ABSTRACT FROM AUTHOR]

Subsampling is a practical strategy for analyzing vast survival data, which are progressively encountered across diverse research domains. While the optimal subsampling method has been applied to inferences for Cox models and parametric accelerated failure time (AFT) models, its application to semi‐parametric AFT models with rank‐based estimation have received limited attention. The challenges arise from the non‐smooth estimating function for regression coefficients and the seemingly zero contribution from censored observations in estimating functions in the commonly seen form. To address these challenges, we develop optimal subsampling probabilities for both event and censored observations by expressing the estimating functions through a well‐defined stochastic process. Meanwhile, we apply an induced smoothing procedure to the non‐smooth estimating functions. As the optimal subsampling probabilities depend on the unknown regression coefficients, we employ a two‐step procedure to obtain a feasible estimation method. An additional benefit of the method is its ability to resolve the issue of underestimation of the variance when the subsample size approaches the full sample size. We validate the performance of our estimators through a simulation study and apply the methods to analyze the survival time of lymphoma patients in the surveillance, epidemiology, and end results program. [ABSTRACT FROM AUTHOR]

ABSTRACT ‘Treatment effect measures under nonproportional hazards’ by Snapinn et al. (Pharmaceutical Statistics, 22, 181–193) recently proposed some novel estimates of treatment effect for time‐to‐event endpoints. In this note, we clarify three points related to the proposed estimators that help to elucidate their properties. We hope that their work, and this commentary, will motivate further discussion concerning treatment effect measures that do not require the proportional hazards assumption. [ABSTRACT FROM AUTHOR]

Introduction Methods Findings Discussion Arteriovenous fistulas (AVFs) and grafts are essential for long‐term hemodialysis access in patients with end‐stage renal disease. However, complications and access failure often necessitate re‐intervention. In this study, we aim to delineate the factors associated with earlier failure of permanent hemodialysis access warranting revision procedures.This retrospective study aimed to identify factors associated with increased revision rates in AVFs and arteriovenous grafts, using multivariate survival analysis. A cohort of 136 patients who underwent initial arteriovenous access creation between 2005 and 2022 was analyzed. Patient characteristics, including age, comorbidities, access type, and vascular anatomy, were extracted, and hazard ratios (HR) were calculated to identify independent predictors of needing revision.A total of 119 patients were included in the final cohort, with a mean age of 55.2 years. Over 40% of patients had a previous central venous catheter placement, while 15% had a previous AVF. The majority of procedures were performed on the left side (74%), and brachiocephalic fistulas were most commonly created (41%). Univariate and multivariate Cox regression revealed that age (adjusted HR = 1.02, p = 0.01) and prior central venous catheter placement (adjusted HR = 1.77, p = 0.01) were independent predictors of earlier revision, while other variables such as sex, hypertension, and diabetes did not show significant associations. Patients with prior central venous catheter placement had a 77% increased risk of revision, even when adjusted for confounders.Understanding predictors of successful long‐term access outcomes can guide decision‐making regarding access type and alternative strategies. In our cohort, increased age and prior central venous catheter placement are associated with a shorter time to failure of permanent hemodialysis access and an increased risk of needing revision. [ABSTRACT FROM AUTHOR]

Survival analysis in medical research has witnessed a growing interest in applying deep learning techniques to model complex, high-dimensional, heterogeneous, incomplete, and censored data. Current methods make assumptions about the relations between data that may not be valid in practice. Therefore, we introduce SAVAE (Survival Analysis Variational Autoencoder). SAVAE, based on Variational Autoencoders, contributes significantly to the field by introducing a tailored Evidence Lower BOund formulation, supporting various parametric distributions for covariates and survival time (if the log-likelihood is differentiable). It offers a general method that demonstrates robustness and stability through different experiments. Our proposal effectively estimates time-to-event, accounting for censoring, covariate interactions, and time-varying risk associations. We validate our model in diverse datasets, including genomic, clinical, and demographic tabular data, with varying levels of censoring. This approach demonstrates competitive performance compared to state-of-the-art techniques, as assessed by the Concordance Index and the Integrated Brier Score. SAVAE also offers an interpretable model that parametrically models covariates and time. Moreover, its generative architecture facilitates further applications such as clustering, data imputation, and synthetic patient data generation through latent space inference from survival data. This approach fosters data sharing and collaboration, improving medical research and personalized patient care. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common cause of cancer death and is a frequently diagnosed cancer among women worldwide. It is becoming a challenging health condition in Ethiopia with a high rate of morbidity and mortality. The main aim of this study was to model the time to death in breast cancer patients at Hiwot Fana Specialized University Hospital. A retrospective cohort study was carried out from April 1st, 2020, to April 1st, 2023, and 296 women were included in the study. We used nonparametric methods and Bayesian accelerated failure time models (with Laplace approximation) to identify risk factors and choose a model fitting breast cancer patient data. Model comparison was performed using the marginal likelihood, deviance information criterion and Watanabe Akaike information criterion. From the total of 296 patients in the study, 56 (18.9%) died. The estimated median survival time was 33 months. The log-rank test showed that age group, stage, alcohol consumption, smoking habit, and comorbidity were potential risk factors associated with the time to death in breast cancer patients at the 5% level of significance. The Bayesian Weibull accelerated failure time model was found to be the best fitted model for predicting the survival time of patients with minimum DIC (520.39) and WAIC (521.59) values. The final Bayesian Weibull AFT model with the integrated nested Laplace approximation estimation technique revealed that age group, stage, alcohol consumption, smoking habit, and comorbidity were significantly associated with the time to death in breast cancer patients. Individuals older than 65 years, with stage IV disease, drinking alcohol, smoking cigarettes and having comorbidities had shortened survival times in patients with breast cancer. Hence, Hiwot Fana Specialized University Hospital and related bodies should work on awareness creation to reduce smoking habits and alcohol use as well as give due attention to elderly and stage IV breast cancer patients during intervention. [ABSTRACT FROM AUTHOR]

ABSTRACT An accelerated failure time (AFT) model assumes a log‐linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are directly on failure times, the interpretation of which is intuitive. The semiparametric AFT model that does not specify the error distribution is sufficiently flexible and robust to depart from the distributional assumption. Owing to its desirable features, this class of model has been considered a promising alternative to the popular Cox model in the analysis of censored failure time data. However, in these AFT models, a linear predictor for the mean is typically assumed. Little research has addressed the non‐linearity of predictors when modeling the mean. Deep neural networks (DNNs) have received much attention over the past few decades and have achieved remarkable success in a variety of fields. DNNs have a number of notable advantages and have been shown to be particularly useful in addressing non‐linearity. Here, we propose applying a DNN to fit AFT models using Gehan‐type loss combined with a sub‐sampling technique. Finite sample properties of the proposed DNN and rank‐based AFT model (DeepR‐AFT) were investigated via an extensive simulation study. The DeepR‐AFT model showed superior performance over its parametric and semiparametric counterparts when the predictor was nonlinear. For linear predictors, DeepR‐AFT performed better when the dimensions of the covariates were large. The superior performance of the proposed DeepR‐AFT was demonstrated using three real datasets. [ABSTRACT FROM AUTHOR]

Background Results Conclusion The coffee crop is prominent in Brazilian agriculture, making the country a global power in this area. One of the main concerns in the coffee sector is disease, which can affect coffee productivity and quality. Thus, it is important to evaluate the factors that may affect coffee quality and thus enhance the development of strategies to reduce coffee losses and costs and optimize production. This study evaluated the influence of the type of irrigation (self‐propelled, drip, and center pivot) on the time until the occurrence of phoma leaf spot on Arabica coffee plants, considering the intensity of the disease. Additionally, the association between longitudinal incidence and the time until an event of interest was assessed based on the joint modeling of longitudinal and survival data.The results of this study identify the effectiveness of drip irrigation system compared with other systems; the use of such systems was associated with an ~46.5% reduction in the risk of leaf spot disease compared with the use of a self‐propelled irrigation system. The use of a center pivot system increased the risk of disease progression compared with a self‐propelled system. An association between the longitudinal and survival processes was also observed.The findings demonstrate the superior performance of the drip irrigation system in controlling phoma leaf spot disease in Arabica coffee plants compared with self‐propelled and center pivot systems. This research highlights the potential of using drip irrigation to establish more effective agricultural practices in coffee cultivation, contributing to better disease management and improved crop quality. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Short-term rental (STR) platforms like Airbnb have significantly impacted urban housing sustainability, particularly in cities like New York City. The COVID-19 pandemic disrupted the STR market, raising questions about its resilience and effects on sustainable urban housing. This study addresses the following research questions: (1) How did unit and neighborhood characteristics influence the survival of Airbnb listings during the pandemic? (2) What changes occurred in the factors determining the emergence of new listings during the pandemic? Using data from Inside Airbnb, we applied Cox proportional hazard models and negative binomial regression to analyze changes before and after the pandemic. We found that during the pandemic, price discounts became crucial for listing survival, while traditional quality indicators like superhost status and high ratings lost significance. The importance of subway accessibility decreased, reflecting shifts in traveler preferences. Additionally, new listings were less likely to emerge in high-density Airbnb areas and more likely in neighborhoods with higher crime rates. These findings highlight the need for sustainable regulatory approaches that balance the benefits of STR platforms with protecting housing affordability and community well-being. Our study provides insights for policymakers aiming to promote sustainable urban housing during global crises. [ABSTRACT FROM AUTHOR]

Introduction: The clinicopathological characteristics and prognosis of placental site trophoblastic tumor (PSTT) and epithelioid trophoblastic tumor (ETT) have not been well summarized. Consequently, we conducted the largest to date series of samples of both types and employed machine learning (ML) to assess treatment effectiveness and develop accurate prognostic models for patients with GTN. Gestational choriocarcinoma (GCC) was used as the control group to show the clinical features of PTSS and ETT. Methods: The Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five ML algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of the ML models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. Results: The study population comprised 725 patients. Among them, 139 patients had ETT, 107 had PSTT, and 479 had GCC. There were no significant differences in survival between the different tumor groups. Multivariate Cox regression analysis revealed that metastasis was a significant prognostic factor for GCC, while older age and radiotherapy were significant prognostic factors for PTSS and ETT. ML models revealed that the Gradient Boosting classifier accurately predicted the outcomes, followed by the random forest classifier, K-Nearest Neighbors, Logistic Regression, and multilayer perceptron models. The most significant contributing factors were tumor size, year of diagnosis, age, and race. Discussion: Our study provides a method for treatment and prognostic assessment of patients with GTN. The ML we developed can be used as a convenient individualized tool to facilitate clinical decision making. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is the third most frequently diagnosed cancer worldwide. Diabetes and CRC share many overlapping lifestyle risk factors such as obesity, heavy alcohol use, and diet. This study aims to develop a risk scoring system for CRC prediction among diabetes patients using routine medical records. Methods: A retrospective cohort study was conducted using electronic health records of Hong Kong. Patients who received diabetes care in public general outpatient clinics between 2010 and 2019 and had no cancer history were identified, and followed up until December 2019. The outcome was diagnosis of CRC during follow-up. For model building, predictors were first selected using random survival forest, and weights were subsequently assigned to selected predictors using Cox regression. Results: Of the 386,325 patients identified, 4,199 patients developed CRC during a median follow-up of 6.2 years. The overall incidence rate of CRC was 1.93 per 1000 person-years. In the final scoring system, age, waist-to-hip ratio, and serum creatinine were included as predictors. The C-index on test set was 0.651 (95%CI: 0.631-0.669). Elevated serum creatinine (=127 µmol/L) could be a potential important predictor of increased CRC risk. Conclusion: While obesity is a well-known risk factor for CRC, renal dysfunction could be potentially linked to an elevated risk of CRC among diabetes patients. Further studies are warranted to explore whether renal function could be a potential parameter to guide screening recommendation for diabetes patients. [ABSTRACT FROM AUTHOR]

Background: Non-specific interstitial pneumonia (NSIP) is an interstitial lung disease that can result in end-stage fibrosis. We investigated the influence of body composition and pulmonary fat attenuation volume (CTpfav) on overall survival (OS) in NSIP patients. Methods: In this retrospective single-center study, 71 NSIP patients with a median age of 65 years (interquartile range 21.5), 39 females (55%), who had a computed tomography from August 2009 to February 2018, were included, of whom 38 (54%) died during follow-up. Body composition analysis was performed using an open-source nnU-Net-based framework. Features were combined into: Sarcopenia (muscle/bone); Fat (total adipose tissue/bone); Myosteatosis (inter-/intra-muscular adipose tissue/total adipose tissue); Mediastinal (mediastinal adipose tissue/bone); and Pulmonary fat index (CTpfav/lung volume). Kaplan–Meier analysis with a log-rank test and multivariate Cox regression were used for survival analyses. Results: Patients with a higher (> median) Sarcopenia and lower (< median) Mediastinal Fat index had a significantly better survival probability (2-year survival rate: 83% versus 71% for high versus low Sarcopenia index, p = 0.023; 83% versus 72% for low versus high Mediastinal fat index, p = 0.006). In univariate analysis, individuals with a higher Pulmonary fat index exhibited significantly worse survival probability (2-year survival rate: 61% versus 94% for high versus low, p = 0.003). Additionally, it was an independent risk predictor for death (hazard ratio 2.37, 95% confidence interval 1.03–5.48, p = 0.043). Conclusion: Fully automated body composition analysis offers interesting perspectives in patients with NSIP. Pulmonary fat index was an independent predictor of OS. Relevance statement: The Pulmonary fat index is an independent predictor of OS in patients with NSIP and demonstrates the potential of fully automated, deep-learning-driven body composition analysis as a biomarker for prognosis estimation. Key Points: This is the first study assessing the potential of CT-based body composition analysis in patients with non-specific interstitial pneumonia (NSIP). A single-center analysis of 71 patients with board-certified diagnosis of NSIP is presented Indices related to muscle, mediastinal fat, and pulmonary fat attenuation volume were significantly associated with survival at univariate analysis. CT pulmonary fat attenuation volume, normalized by lung volume, resulted as an independent predictor for death. [ABSTRACT FROM AUTHOR]

Background: Gaucher’s disease (GD), a lysosomal storage disorder, poses significant treatment challenges. This 23-year study assesses survival rates and treatment efficacy in Brazilian GD patients, integrating data from a 16-year cohort (2000–2015) and the TABNET/DATASUS medicines distribution data (1999–2022). Objective: To investigate the survival of GD patients in Brazil, identifying key risk factors and evaluating the impact of treatments funded by the Brazilian National Health System (SUS). Methodology: A 16-year retrospective cohort study was conducted using the National Database of SUS. Patients diagnosed with GD and treated with Enzyme Replacement Therapy (ERT) or Substrate Synthesis Inhibition (SSI) from 2000 to 2015 were included. Survival analysis was performed using Kaplan-Meier method and Cox proportional hazards model. The data from TABNET/DATASUS system from 1999 to 2022 was used to assess the trend in drug distribution beyond the main cohort. Results: The study included 1,234 patients. Survival rates at 5 and 10 years were 93.2% and 88.5%, respectively, with age and comorbidities like diabetes, cardiovascular diseases, and Parkinson’s disease significantly affecting survival. Patients who received doses lower than DDD (n = 880) demonstrated a survival probability of 91.8%. In contrast, those with doses equal to the DDD (n = 15) showed a 100% survival probability, as no events were observed in this group. The greater than DDD group (n = 339) exhibited a survival probability of 81%. A logrank test indicated a borderline statistical significance (p = 0.058) in the survival distributions among the different DDD adherence, with the lower dose group showing a favorable trend. Conclusion: This study provides insights into the survival rates and associated risk factors for GD patients in Brazil, contributing to the global understanding of GD and its management. While we acknowledge the inherent limitations of relying largely on electronic medical records and categorical codes, our findings underscore the need for early diagnosis, timely initiation of treatment, effective management of comorbidities, and personalized dosing strategies to improve patient outcomes. Future studies should aim to incorporate clinical verification of electronic data to further enhance the reliability and applicability of these findings. [ABSTRACT FROM AUTHOR]

ABSTRACT Objectives Methods Results Conclusion Warm autoimmune hemolytic anemia (wAIHA) is a rare autoantibody‐mediated disorder, and first‐line treatment primarily relies on corticosteroids. This study assessed overall survival (OS) and treatment patterns of wAIHA in Sweden.Adults with ≥ 1 primary diagnosis code for wAIHA (or AIHA plus oral corticosteroids (OCS)/immunosuppressants as sensitivity analyses) between 2011 and 2022 were identified from five Swedish national registers and linked through each patient's unique identity number. Kaplan–Meier curves with log‐rank tests and Cox regressions were performed to assess OS for patients with primary versus secondary wAIHA and patients with wAIHA and long‐term versus short‐term (≥ 3 vs. < 3 months) OCS users.The main analysis included 292 patients; 1791 patients were included in the sensitivity analysis. At a median 3.7‐year follow‐up, a median OS in primary wAIHA was not reached versus 6.0 years for secondary wAIHA (log‐rank test: p = 0.003). Subgroup analyses showed no significant difference in risk of death between long‐term and short‐term OCS users; however, in the sensitivity analysis, long‐term OCS users showed significantly higher risk of death (adjusted hazard ratio: 1.45; 95% confidence interval: 1.180, 1.781; p < 0.001) versus short‐term OCS users.Secondary wAIHA or long‐term OCS use was associated with lower OS, underscoring the disease burden and unmet need for efficacious wAIHA treatments. [ABSTRACT FROM AUTHOR]

Background: Pulmonary rehabilitation (PR) is widely recommended for short-term benefits in chronic respiratory diseases, yet long-term outcomes remain uncertain. This retrospective cohort study addresses this gap, comparing 20-year mortality rates between PR participants and matched controls, and hypothesizing that the short-term benefits of PR contribute to improved long-term survival. Methods: The 20-year mortality of stable chronic respiratory patients who participated in an outpatient PR program was compared with a matched control group based on the type of lung disease. Demographic and clinical variables, and the dates of deaths, were extracted and compared between two groups with two sample t-test and chi-square tests. Kaplan-Meier plots and Cox regression analyses were employed to evaluate survival differences. Results: Between 2000 and 2002, 238 individuals enrolled in a pulmonary rehabilitation (PR) program (58% male, mean age ± SD: 69 ± 8 years, mean FEV1% predicted ± SD: 46 ± 21%). An equal number of people with comparable lung disease were selected as controls (88% COPD, 5% ILD). Controls had lower FEV1% predicted values (mean ± SD: 39 ± 17%, P < 0.001), smoked more (mean ± SD: 48 ± 35 pack-years, P = 0.032), and no differences in age, BMI, sex, and Index of Relative Socio-economic Advantage and Disadvantage (IRSAD). Median (IQR) follow-up time was 68 months (34–123), with 371 (78%) deaths. Univariable (HR = 1.71, p < 0.001) and multivariable (HR = 1.64, p < 0.001) Cox regression found higher mortality risk in controls. Subgroup analysis for COPD replicated these findings (HR = 1.70, P < 0.001). Discussion: Despite some methodological limitations, our study suggests that clinically stable patients with chronic respiratory disease who undertake PR may have lower mortality than matched controls. Trial registration: Retrospectively registered. [ABSTRACT FROM AUTHOR]

Background: Currently, the indications for pulpectomy of primary molars performed under dental general anesthesia vary across countries. Therefore, we retrospectively investigated the five-year survival rate of primary molars following pulpectomy performed under dental general anesthesia and the impact of this treatment on permanent successors, assessed the risk factors related to overall survival and clarified the indications for pulpectomy. Methods: The medical records of children receiving pulpectomy of primary molars under dental general anesthesia from August 1, 2013, to November 30, 2023, were reviewed. Potential risk factors, including gender, age, general health, tooth type, tooth location, endodontic diagnosis and quality of root filling, were assessed via univariate and multivariate Cox proportional hazards regression models, and the survival rate was examined via the Kaplan‒Meier technique. Moreover, the rate of resorption of the root canal filling materials, degree of resorption of the overfilled/over-extended root canal filling materials and development of permanent successors were assessed by clinical and radiographic examination. Results: The study included 320 teeth from 161 children (86 boys and 75 girls). The overall five-year survival rate was 38.2%, and the mean overall survival time was 54.2 months. Endodontic diagnosis was considered a significant risk factor (P < 0.05). In the first, second and third years, 57.4%, 81.8%, and 94.8%, respectively, of obturation materials in the root canals were resorbed at a faster rate than the roots. There was an altered eruption direction in 7 permanent teeth, and 4 permanent teeth were diagnosed with enamel hypoplasia. Conclusions: In this study, the 60-month survival rate of primary molars treated by pulpectomy under dental general anesthesia was 38.32%. Operators should have an accurate assessment of the status of the pulp, have a strict grasp of the preoperative indications and select the appropriate treatment method according to the guidelines. Individual cases suggest overfilling, overextension and periapical periodontitis in primary molars have an impact on enamel hypoplasia and altered eruption direction in permanent teeth. [ABSTRACT FROM AUTHOR]

This study investigates the prognostic significance of SH3 and multiple ankyrin repeat domains 2 (SHANK2) gene expression in glioma patients, using data from The Cancer Genome Atlas (TCGA), the Genotype‐Tissue Expression (GTEx) project, and the Gene Expression Omnibus (GEO). Through comprehensive analysis, we found a significant association between higher SHANK2 expression and improved survival outcomes across various glioma subtypes. To further validate the clinical relevance of SHANK2, we conducted cellular experiments involving siRNA‐mediated knockdown of SHANK2 in U87 and A172 glioma cell lines. Quantitative real‐time PCR (qPCR) and Western blot analyses confirmed the successful knockdown of SHANK2, and subsequent MTT assays revealed that silencing SHANK2 significantly promoted glioma cell proliferation. These findings underscore the potential role of SHANK2 as a tumor suppressor in glioma. The study also introduces a multivariate prognostic model incorporating SHANK2, providing a novel perspective on glioma prognosis. While the retrospective nature of the study presents limitations, our results suggest that SHANK2 expression could serve as a valuable biomarker for glioma prognosis and inform future therapeutic strategies. [ABSTRACT FROM AUTHOR]

Background: Methylation-related signatures play crucial roles in tumorigenesis and progression. However, their roles in the immune response in primary glioblastoma (GBM) remains unclear. Methods: We analyzed the differential expression of specific members of T cell exhaustion-related pathways in GBM from the perspective of T cell exhaustion. We further screened for significantly negatively correlated methylation sites as candidate methylation markers for T cell exhaustion. Using consensus clustering, we divided the samples into two categories with significant differences in overall survival (OS). We then performed univariate and multivariate Cox regression analyses to construct the T Cell Exhaustion Methylation (TEXM) signature. Finally, we confirmed that this signature served as an independent prognostic factor, and further characterized it in terms of drug resistance and immunotherapy. Results: We identified 95 significantly differentially expressed T cell exhaustion-related genes and 51 methylation markers associated with T cell exhaustion. The cancer samples were classified according to methylation site markers, thus indicating two subtypes with significant differences in OS: subtype A and subtype B. Tumor scores, stromal scores, tumor purity, and ESTIMATE scores all showed significant differences between subtypes (P < 0.05). Univariate Cox regression analysis identified five methylation sites significantly associated with OS, and multivariate Cox regression analysis was used to construct the TEXM signature model by using these five methylation sites. Significant differences in OS were found between the groups with high and low TEXM signature scores, on the basis of calculation of the TEXM signature scores of tumor samples and using the median score to divide them into high and low score groups. Survival analysis revealed that the high score group had poorer OS and DFS than the low score group in the validation set. Notably, we observed a significant difference in drug sensitivity between the high and low TEXM signature score groups, with the high score group showing higher drug resistance and poorer prognosis. The tumor immune state, as predicted with Tracking Tumor Immunophenotype (TIP), revealed significant differences in antitumor immune scores between the high and low TEXM signature score groups. Finally, we identified 43 significantly differentially regulated metabolism-associated biological processes. Conclusion: The epigenetic methylation-related TEXM signature plays a key role in driving differential immune responses in GBM. [ABSTRACT FROM AUTHOR]

Objective: To retrospectively analyze the characteristics of newly diagnosed marginal zone lymphoma (MZL) patients, evaluate the efficacy of different treatment regimens, and explore prognostic factors in the era of immunotherapy. Methods: We reviewed the clinical data of newly diagnosed MZL patients treated at the Department of Hematology, The First Hospital of Jilin University, from October 2013 to October 2023. Survival differences between groups were analyzed using the log-rank test, and prognostic factors were identified. Results: A total of 265 newly diagnosedMZL patients were included, with a median age of 59 years (range 22-90). The most common pathological type was mucosaassociated lymphoid tissue (MALT) lymphoma, accounting for 66.0% of cases. Among the 147 MZL patients included in the efficacy analysis, the median followup was 43.4 months. Both the median progression-free survival (PFS) and overall survival (OS) were not reached. The 5-year PFS and OS rates were 76.0% and 86.6%, respectively. Patientswho achieved complete response (CR) after induction therapy had significantly better PFS (P=0.0045), OS (P<0.001), and time to next treatment (TTNT) (P=0.0045) compared to those who did not achieve CR. A subgroup analysis was conducted on 51 MZL patients with high tumor burden who received =4 cycles of treatment. It was found that the CR rate (CRR) in patients receiving obinutuzumab (G) ± chemotherapy was significantly higher than in those receiving rituximab (R) ± chemotherapy (93.8% vs. 48.6%, P=0.002). Multivariate analysis revealed that disease progression or death within 24 months of initial treatment (POD24) was an independent risk factor affecting OS (P<0.001). Patients who experienced POD24 had a median survival of only 19.7 months, with a 3-year OS rate of just 37.6%, whereas those without POD24 had a 3-year OS rate of 97.3%. Conclusion: MZL is predominantly seen in middle-aged and elderly patients and is a specific indolent B-cell lymphoma, with MALT lymphoma being the most common subtype. Achieving CR after induction therapy significantly prolongs survival in MZL patients. Compared to R ± chemotherapy, G ± chemotherapy achieves a higher CRR in high tumor burden MZL patients. In the era of immunotherapy, POD24 is an independent prognostic factor for MZL. [ABSTRACT FROM AUTHOR]

In the disciplines of medicine, biology and social science, causal mediation analysis is widely used to examine the effect of an exposure on an outcome through different causal pathways. Recently, there is rapid development of mediation analysis with survival data. An early work of Lange and Hansen proposed to identify the direct and indirect causal effects under the additive hazards model with a single mediator. However, inaccurate measurements of mediators and confounders may lead to biased causal effect estimation. We propose a measurement error correction approach to tackle measurement error in the mediators and confounders under the additive hazards model where multiple mediators are present. We apply the proposed corrected method to the ACTG175 Study data set, and uncover interesting findings. [ABSTRACT FROM AUTHOR]

To evaluate the role of amphiregulin (AREG), a ligand of epidermal growth factor receptor, in breast invasive carcinoma (BRIC) the present study was initiated. For this purpose, we used freely available GeneCards Suite for AREG enrichment analysis, the online UALCAN web portal for analysis of differential expression and promoter's methylation status of AREG, the online cBioPortal cancer genome atlas to document carcinoma-associated AREG mutations and CCLE (Cancer Cell Line Encyclopedia) and GDSC (Genomics of Drug Sensitivity in Cancer) toolkit to document AREG's sensitivity towards various anti-cancer drugs. We observed lower expression of AREG in the majority of patients, in comparison to normal controls, due to promoter's hypermethylation. While AREG's upregulation was examined in premenopausal condition, stage 1 and a few other patient groups. The reduced expression of AREG in human epidermal growth factor receptor 2 (HER2) positive, triple-negative and patients of many other subtypes highlights the relevance of AREG with the progression of the disease. Moreover, the AREG's expression improved the effectiveness of anti-neoplastic drugs but confers resistance against gamma-secretase inhibitors. Patients exhibiting lower/medium levels of AREG's expression had better survival chances. In the future, an investigation of the factors which modulate methylation patterns of AREG's promoter and an evaluation of the prognostic power of AREG's expression will facilitate the identification of novel therapeutic channels for better prognosis of BRIC and devise a multilayered treatment strategy. [ABSTRACT FROM AUTHOR]

Background: Metastatic colorectal cancer (mCRC) constitutes a significant health burden globally, accompanied by elevated mortality rates. This study aimed to assess the cost-effectiveness of regorafenib, an orally administered multi-kinase inhibitor, compared to the combination of Cetuximab and Irinotecan (CetIri) as third-line therapy for mCRC in Iran. Method: A model-based cost-utility analysis was conducted employing a semi- Markov model for a hypothetical cohort of 1,000 patients, integrating time-dependent transition probabilities. From the perspective of the Iranian healthcare payer, the analysis included direct medical costs, such as therapy, monitoring, and adverse effectrelated expenses, sourced from national databases in Iran. A yearly discount rate of 5% was applied to both costs and outcomes. Data analysis utilized Microsoft Excel, R version 4.1.3, and TreeAge Pro Healthcare version 2022 software, with the significance threshold set at 0.05. Results: The base-case analysis revealed that regorafenib offers a cost saving of $12,154 and an incremental gain of 0.1 quality-adjusted life years per patient over a 19-month horizon compared with the CetIri regimen. Probabilistic sensitivity analysis showed a greater than 99% probability of regorafenib being cost-effective. Conclusion: Consistent with existing evidence, the findings advocate regorafenib as a cost-effective alternative to CetIri for third-line treatment of mCRC in Iran, considering the specific healthcare system context. Given the foundational assumptions, caution is advised when extrapolating these results to other regions. [ABSTRACT FROM AUTHOR]

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies. [ABSTRACT FROM AUTHOR]

We develop a Bayesian semiparametric model for the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in a phase III clinical trial in which patients move through a sequence of four treatment courses. At each course, they undergo treatment that may or may not include anthracyclines (ACT). While ACT is known to be effective at treating AML, it is also cardiotoxic and can lead to early death for some patients. Our task is to estimate the potential survival probability under hypothetical dynamic ACT treatment strategies, but there are several impediments. First, since ACT is not randomized, its effect on survival is confounded over time. Second, subjects initiate the next course depending on when they recover from the previous course, making timing potentially informative of subsequent treatment and survival. Third, patients may die or drop out before ever completing the full treatment sequence. We develop a generative Bayesian semiparametric model based on Gamma Process priors to address these complexities. At each treatment course, the model captures subjects' transition to subsequent treatment or death in continuous time. G-computation is used to compute a posterior over potential survival probability that is adjusted for time-varying confounding. Using our approach, we estimate the efficacy of hypothetical treatment rules that dynamically modify ACT based on evolving cardiac function. [ABSTRACT FROM AUTHOR]

Drug overdose death rates are the highest recorded in New York City (NYC). Substance use disorder (SUD) treatment termination can confer increased risk of drug overdose death. Our objective was to determine the probability of, and factors associated with, drug overdose death following SUD treatment termination. Using a retrospective longitudinal cohort design, we identified those who had NYC-based SUD treatment terminated (01/2016–06/2019) using Chief Medical Examiner and SUD treatment data. Using survival analyses, we examined drug overdose deaths ≤ 14 and ≤ 90 days following SUD treatment termination, respectively. Of 51,171 patients with SUD treatment termination, 140 and 342 had a drug overdose death < 14 and ≤ 90 days, respectively. The crude drug overdose death rate was 26.7 per 1000 person-years at-risk in the ≤ 90-day period and was 71.6 per 1000 person-years at-risk in the ≤ 14-day period. In adjusted Cox proportional hazard model examining death ≤ 14 days, those unemployed (compared to employed) and those terminated from residential treatment (compared to medically supervised withdrawal, opioid treatment programs, and outpatient treatment) were more likely to have had a drug overdose death (all p-values < 0.01). In adjusted Cox proportional hazard model examining death ≤ 90 days, non-Hispanic White people (compared to non-Hispanic Black people), those not stably housed (compared to stably housed), those unemployed and those terminated from residential treatment were more likely to have had a drug overdose death (all p-values < 0.01). Strategies to improve retention including the reassessment of program treatment termination criteria along with strategies to promote ongoing OUD treatment, engagement in harm reduction, and distribution of naloxone are needed. [ABSTRACT FROM AUTHOR]

ABSTRACT Many important questions in infectious disease epidemiology involve associations between covariates (e.g., age or vaccination status) and infectiousness or susceptibility. Because disease transmission produces dependent outcomes, these questions are difficult or impossible to address using standard regression models from biostatistics. Pairwise survival analysis handles dependent outcomes by calculating likelihoods in terms of contact interval distributions in ordered pairs of individuals. The contact interval in the ordered pair ij$$ ij $$ is the time from the onset of infectiousness in i$$ i $$ to infectious contact from i$$ i $$ to j$$ j $$, where an infectious contact is sufficient to infect j$$ j $$ if they are susceptible. Here, we introduce a pairwise accelerated failure time regression model for infectious disease transmission that allows the rate parameter of the contact interval distribution to depend on individual‐level infectiousness covariates for i$$ i $$, individual‐level susceptibility covariates for j$$ j $$, and pair‐level covariates (e.g., type of relationship). This model can simultaneously handle internal infections (caused by transmission between individuals under observation) and external infections (caused by environmental or community sources of infection). We show that this model produces consistent and asymptotically normal parameter estimates. In a simulation study, we evaluate bias and confidence interval coverage probabilities, explore the role of epidemiologic study design, and investigate the effects of model misspecification. We use this regression model to analyze household data from Los Angeles County during the 2009 influenza A (H1N1) pandemic, where we find that the ability to account for external sources of infection increases the statistical power to estimate the effect of antiviral prophylaxis. [ABSTRACT FROM AUTHOR]

Prior research measures immigration by only accounting for where immigrants live. We argue that this approach misses the activity spaces of immigrants, which also impact crime but are not always located in their residential communities. The present study uses an alternative definition of immigration—immigrant-ethnic activity space (IEAS)—that accounts for both the residential location and routine activities of immigrants. Additionally, given the crime-reducing effects associated with immigration, including for high-risk populations, we consider whether IEAS protects against reoffending for ex-inmates. Using Cox hazards models, we examine the relationship between IEAS and recidivism across the communities of five ethnic groups. Results show that the IEAS of all groups are inversely associated with recidivism. However, ex-prisoner concentration amplifies the risk for recidivism in the IEAS of some groups. [ABSTRACT FROM AUTHOR]

Aims: To investigate the impact of various clinical factors associated with Graves' disease on the success rate of radioiodine (RAI) therapy for Graves' disease within 3 years, and to determine the optimal range of iodine dosage per unit volume that yields the highest cure rate for Graves' disease within 1 year. Materials and Methods: This retrospective study included patients diagnosed with Graves' disease who underwent RAI therapy at the Second Affiliated Hospital of Anhui Medical University between October 2012 and October 2022. The cumulative success rate was analysed using the Kaplan–Meier method. Univariate and multivariate Cox proportional hazards regression models were employed to evaluate factors associated with successful treatment of Graves' disease. Outcomes were categorized as either success or failure for all patients. Results: Overall, 1994 patients were enrolled in this study, including 594 (29.8%) male and 1399 (70.2%) female patients. The success and failure groups comprised 1645 (82.4%) and 349 patients (17.6%), respectively, after a 3‐year follow‐up period. Multivariate regression analysis demonstrated that sex, antithyroid drug (ATD) use before RAI therapy, age, thyroid receptor antibody (TRAb) levels, iodine dose, thyroid mass, and early ATD use before RAI therapy were independent influencing factors for Graves' disease cure. Conclusions: We found that female patients and those with TRAbs ≥31.83 IU/L and thyroid mass ≥ 73.42 g had a lower cure rate. Therefore, thyroid size, disease severity, and duration of disease should be comprehensively considered when making treatment decisions and iodine dose selection in clinical practice. [ABSTRACT FROM AUTHOR]

In observational/field studies, competing risks and left-truncation may co-exist, yielding 'left-truncated competing risks' settings. Under the assumption of independent competing risks, parametric estimation methods were developed for left-truncated competing risks data. However, competing risks may be dependent in real applications. In this paper, we propose a Bayesian estimator for both independent competing risks and copula-based dependent competing risks models under left-truncation. The simulations show that the Bayesian estimator for the copula-based dependent risks model yields the desired performance when competing risks are dependent. We also comprehensively explore the choice of the prior distributions (Gamma, Inverse-Gamma, Uniform, half Normal and half Cauchy) and hyperparameters via simulations. Finally, two real datasets are analyzed to demonstrate the proposed estimators. [ABSTRACT FROM AUTHOR]

Background: Tuberous sclerosis (TSC)–associated kidney disease is a leading cause of mortality in adults with TSC. This study aimed to understand TSC features in children, particularly kidney involvement, to inform clinical care for this specific group. Methods: This retrospective cohort study included all paediatric (< 19 years) TSC cases at a large tertiary paediatric nephrology centre. Relevant data were collected from patients' records, statistical analyses were performed to identify associations between variables, survival probabilities were estimated with Kaplan‒Meier curves, and log-rank tests were conducted to assess survival differences among genetic mutations. Results: A total of 182 children with TSC were included. Among the 145 children with available kidney imaging data, 78.6% (114/145) exhibited kidney lesions. Angiomyolipomas (AMLs) were significantly more prevalent in the TSC2 mutation group (p = 0.018). Children with TSC2 mutations generally had poorer lesion-free survival than those with TSC1 mutations, but this difference was only significant for AMLs (p = 0.030). The change in size of largest AMLs increased with age and doubled in children above 9 years; a similar pattern was observed when stratified by genetic mutation. In contrast, kidney cysts exhibited two peaks: one in children under 5 years (2.31 mm/year) and the second in children between 15–19 years (2.82 mm/year). Chronic kidney disease was observed in 12.3% (10/81) of children, and high-risk AMLs above 3 cm were observed in 9% (13/145). Conclusions: While TSC kidney disease emerges later in the disease course than neurological features, our findings emphasise the importance of kidney surveillance during childhood, including routine kidney imaging, kidney function, and blood pressure monitoring. [ABSTRACT FROM AUTHOR]

The proportional hazard regression models are widely used statistical tools for analyzing survival data and estimating the effects of covariates on survival times. It is assumed that the effects of the covariates are constant across the time. In this paper, we propose a novel extension of the proportional hazard model by incorporating an exponentiated-Weibull distribution to model the baseline line hazard function. The proposed model offers more flexibility in capturing various shapes of failure rates and accommodates both monotonic and non-monotonic hazard shapes. The performance evaluation of the proposed model and comparison with other commonly used survival models including the generalized log–logistic, Weibull, Gompertz, and exponentiated exponential PH regression models are explored using simulation results. The results demonstrate the ability of the introduced model to capture the baseline hazard shapes and to estimate the effect of covariates on the hazard function accurately. Furthermore, two real survival medical data sets are analyzed to illustrate the practical importance of the proposed model to provide accurate predictions of survival outcomes for individual patients. Finally, the survival data analysis reveal that the model is a powerful tool for analyzing complex survival data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Prevention & Treatment For Stomatological Diseases is the property of Journal of Prevention & Treatment For Stomatological Diseases Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Prevention & Treatment For Stomatological Diseases is the property of Journal of Prevention & Treatment For Stomatological Diseases Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The analysis of multiple time‐to‐event outcomes in a randomized controlled clinical trial can be accomplished with existing methods. However, depending on the characteristics of the disease under investigation and the circumstances in which the study is planned, it may be of interest to conduct interim analyses and adapt the study design if necessary. Due to the expected dependency of the endpoints, the full available information on the involved endpoints may not be used for this purpose. We suggest a solution to this problem by embedding the endpoints in a multistate model. If this model is Markovian, it is possible to take the disease history of the patients into account and allow for data‐dependent design adaptations. To this end, we introduce a flexible test procedure for a variety of applications, but are particularly concerned with the simultaneous consideration of progression‐free survival (PFS) and overall survival (OS). This setting is of key interest in oncological trials. We conduct simulation studies to determine the properties for small sample sizes and demonstrate an application based on data from the NB2004‐HR study. [ABSTRACT FROM AUTHOR]

In survival analysis, it often happens that some individuals, referred to as cured individuals, never experience the event of interest. When analyzing time‐to‐event data with a cure fraction, it is crucial to check the assumption of "sufficient follow‐up," which means that the right extreme of the censoring time distribution is larger than that of the survival time distribution for the noncured individuals. However, the available methods to test this assumption are limited in the literature. In this article, we study the problem of testing whether follow‐up is sufficient for light‐tailed distributions and develop a simple novel test. The proposed test statistic compares an estimator of the noncure proportion under sufficient follow‐up to one without the assumption of sufficient follow‐up. A bootstrap procedure is employed to approximate the critical values of the test. We also carry out extensive simulations to evaluate the finite sample performance of the test and illustrate the practical use with applications to leukemia and breast cancer data sets. [ABSTRACT FROM AUTHOR]

We propose AFTNet, a novel network‐constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation. When using the log‐linear representation, the inference problem becomes a structured sparse regression problem for which we explicitly incorporate the correlation patterns among predictors using a double penalty that promotes both sparsity and grouping effect. Moreover, we establish the theoretical consistency for the AFTNet estimator and present an efficient iterative computational algorithm based on the proximal gradient descent method. Finally, we evaluate AFTNet performance both on synthetic and real data examples. [ABSTRACT FROM AUTHOR]

Examining the host range of emerging invasive insects is essential to assess their invasion potential and to anticipate the negative impacts of their spread. The ongoing North American invasion of spotted lanternfly (SLF) [ Lycorma delicatula (White, 1845)] threatens agricultural, urban, and natural areas. The survival and development of SLF nymphs on Washington navel orange [ Citrus sinensis (L.) Osbeck (Sapindales: Rutaceae)] trees were assessed in a quarantine facility. Results indicated that SLF nymphs can develop to at least the third instar by feeding exclusively on Washington navel orange. This finding suggests that, at least up to the third stage of nymphal development, Washington navel orange might be a suitable host for SLF, highlighting the possibility that this invasive pest represents an unrecognized threat to this globally important crop and possibly to other Citrus species. [ABSTRACT FROM AUTHOR]