Bayesian Survival Model Research Articles

Patient- and Areal-Level Risk Factors Associated With Lung Cancer Mortality in Victoria, Australia: A Bayesian Spatial Survival Analysis.

In Australia, lung cancer is the leading cause of cancer-related deaths. In Victoria, the mortality risk is assumed to vary across Local Government Areas (LGAs) due to variations in socioeconomic advantage, remoteness, and healthcare accessibility. Thus, we applied Bayesian spatial survival models to examine the geographic variation in lung cancer survival in Victoria. Data on lung cancer cases were extracted from the Victorian Lung Cancer Registry (VLCR). To account for spatial dependence and risk factors of survival in lung cancer patients, we employed a Bayesian spatial survival model. Conditional Autoregressive (CAR) prior was assigned to model the spatial dependence. Deviance Information Criterion (DIC), Watanabe Akaike Information Criterion (WAIC), and Log Pseudo Marginal Likelihood (LPML) were used for model comparison. In the final best-fitted model, the Adjusted Hazard Ratio (AHR) with the 95% Credible Interval (CrI) was reported. The outcome variable was the survival status of lung cancer patients, defined as whether they survived or died during the follow-up period (death was our interest). Our study revealed substantial variations in lung cancer mortality in Victoria. Poor Eastern Cooperative Oncology Group (ECOG) performance status, diagnosed at a regional hospital, Small Cell Lung Cancer (SCLC), advanced age, and advanced clinical stage were associated with a higher risk of mortality, whereas being female, presented at Multidisciplinary Team (MDT) meeting, and diagnosed at a metropolitan private hospital were significantly associated with a lower risk of mortality. Identifying geographical disparities in lung cancer survival may help shape healthcare policy to implement more targeted and effective lung cancer care services.

BHAFT: Bayesian heredity-constrained accelerated failure time models for detecting gene-environment interactions in survival analysis.

In addition to considering the main effects, understanding gene-environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high-dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity-constrained accelerated failure time (BHAFT) models for identifying main and interaction (M-I) effects with novel spike-and-slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high-dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa-bayesian/BHAFT.

Bayesian survival analysis with INLA.

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS." In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach.

An Approach to Remaining Life Estimation for Power Transformers with Biased Data

Abstract Power transformers are among the most important equipment in power systems. Estimating the remaining life of transformers is an important issue for energy companies because of the need for planning maintenance and capital expenditures. Traditional methods fail to provide accurate results mainly because data is incomplete, and there is a significant lack of records of equipment scrapped before 2015. Given this, the study provides a new approach to life estimation for power transformers based on survival analysis. First, the data was selected from only the transformers that were still operating after 2015. Then, a Bayesian survival model with a truncated Weibull distribution is developed to fit the biased data. Parameters are, at last, estimated through maximum likelihood estimation. The experimental results indicate that the proposed method performs well.

Assessing the survival time of hospitalized patients in Eastern Ethiopia during 2019-2020 using the Bayesian approach: A retrospective cohort study.

Severe acute malnutrition remains a significant health challenge, particularly in low- and middle-income countries. The aim of this study was to determine the survival time of under-five children with severe acute malnutrition. A retrospective cohort study was conducted at a hospital, focusing on under-five children with severe acute malnutrition. The study included 322 inpatients admitted to the Chiro hospital in Chiro, Ethiopia, between September 2019 and August 2020, whose data was obtained from medical records. Survival functions were analysed using Kaplan‒Meier plots and log-rank tests. The survival time of severe acute malnutrition was further analysed using the Cox proportional hazards model and Bayesian parametric survival models, employing integrated nested Laplace approximation methods. Among the 322 patients, 118 (36.6%) died as a result of severe acute malnutrition. The estimated median survival time for inpatients was found to be 2 weeks. Model selection criteria favored the Bayesian Weibull accelerated failure time model, which demonstrated that age, body temperature, pulse rate, nasogastric(NG) tube usage, hypoglycemia, anemia, diarrhea, dehydration, malaria, and pneumonia significantly influenced the survival time of severe acute malnutrition. This study revealed that children below 24 months, those with altered body temperature and pulse rate, NG tube usage, hypoglycemia, and comorbidities such as anemia, diarrhea, dehydration, malaria, and pneumonia had a shorter survival time when affected by severe acute malnutrition under the age of five. To reduce the death rate of children under 5 years of age, it is necessary to design community management for acute malnutrition to ensure early detection and improve access to and coverage for children who are malnourished.

Exploring the impact of stage and tumor site on colorectal cancer survival: Bayesian survival modeling.

Colorectal cancer is a prevalent malignancy with global significance. This retrospective study aimed to investigate the influence of stage and tumor site on survival outcomes in 284 colorectal cancer patients diagnosed between 2001 and 2017. Patients were categorized into four groups based on tumor site (colon and rectum) and disease stage (early stage and advanced stage). Demographic characteristics, treatment modalities, and survival outcomes were recorded. Bayesian survival modeling was performed using semi-competing risks illness-death models with an accelerated failure time (AFT) approach, utilizing R 4.1 software. Results demonstrated significantly higher time ratios for disease recurrence (TR = 1.712, 95% CI 1.489-2.197), mortality without recurrence (TR = 1.933, 1.480-2.510), and mortality after recurrence (TR = 1.847, 1.147-2.178) in early-stage colon cancer compared to early-stage rectal cancer. Furthermore, patients with advanced-stage rectal cancer exhibited shorter survival times for disease recurrence than patients with early-stage colon cancer. The interaction effect between the disease site and cancer stage was not significant. These findings, derived from the optimal Bayesian log-normal model for terminal and non-terminal events, highlight the importance of early detection and effective management strategies for colon cancer. Early-stage colon cancer demonstrated improved survival rates for disease recurrence, mortality without recurrence, and mortality after recurrence compared to other stages. Early intervention and comprehensive care are crucial to enhance prognosis and minimize adverse events in colon cancer patients.

Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach

BackgroundDespite the significant weight of difficulty, Ethiopia's survival rate and mortality predictors have not yet been identified. Finding out what influences outpatient breast cancer patients' survival time was the major goal of this study.MethodsA retrospective study was conducted on outpatients with breast cancer. In order to accomplish the goal, 382 outpatients with breast cancer were included in the study using information obtained from the medical records of patients registered at the University of Gondar referral hospital in Gondar, Ethiopia, between May 15, 2016, and May 15, 2020. In order to compare survival functions, Kaplan-Meier plots and the log-rank test were used. The Cox-PH model and Bayesian parametric survival models were then used to examine the survival time of breast cancer outpatients. The use of integrated layered Laplace approximation techniques has been made.ResultsThe study included 382 outpatients with breast cancer in total, and 148 (38.7%) patients died. 42 months was the estimated median patient survival time. The Bayesian Weibull accelerated failure time model was determined to be suitable using model selection criteria. Stage, grade 2, 3, and 4, co-morbid, histological type, FIGO stage, chemotherapy, metastatic number 1, 2, and >=3, and tumour size all have a sizable impact on the survival time of outpatients with breast cancer, according to the results of this model. The breast cancer outpatient survival time was correctly predicted by the Bayesian Weibull accelerated failure time model.ConclusionsCompared to high- and middle-income countries, the overall survival rate was lower. Notable variables influencing the length of survival following a breast cancer diagnosis were weight loss, invasive medullar histology, comorbid disease, a large tumour size, an increase in metastases, an increase in the International Federation of Gynaecologists and Obstetricians stage, an increase in grade, lymphatic vascular space invasion, positive regional nodes, and late stages of cancer. The authors advise that it is preferable to increase the number of early screening programmes and treatment centres for breast cancer and to work with the public media to raise knowledge of the disease's prevention, screening, and treatment choices.

Polyphenism predicts actuarial senescence and lifespan in tiger salamanders.

Actuarial senescence (called 'senescence' hereafter) often shows broad variation at the intraspecific level. Phenotypic plasticity likely plays a central role in among-individual heterogeneity in senescence rate (i.e. the rate of increase in mortality with age), although our knowledge on this subject is still very fragmentary. Polyphenism-the unique sub-type of phenotypic plasticity where several discrete phenotypes are produced by the same genotype-may provide excellent study systems to investigate if and how plasticity affects the rate of senescence in nature. In this study, we investigated whether facultative paedomorphosis influences the rate of senescence in a salamander, Ambystoma mavortium nebulosum. Facultative paedomorphosis, a unique form of polyphenism found in dozens of urodele species worldwide, leads to the production of two discrete, environmentally induced phenotypes: metamorphic and paedomorphic individuals. We leveraged an extensive set of capture-recapture data (8948 individuals, 24 years of monitoring) that were analysed using multistate capture-recapture models and Bayesian age-dependent survival models. Multistate models revealed that paedomorphosis was the most common developmental pathway used by salamanders in our study system. Bayesian age-dependent survival models then showed that paedomorphs have accelerated senescence in both sexes and shorter adult lifespan (in females only) compared to metamorphs. In paedomorphs, senescence rate and adult lifespan also varied among ponds and individuals. Females with good body condition and high lifetime reproductive success had slower senescence and longer lifespan. Late-breeding females also lived longer but showed a senescence rate similar to that of early-breeding females. Moreover, males with good condition had longer lifespan than males with poor body condition, although they had similar senescence rates. In addition, late-breeding males lived longer but, unexpectedly, had higher senescence than early-breeding males. Overall, our work provides one of the few empirical cases suggesting that environmentally cued polyphenism could affect the senescence of a vertebrate in nature, thus providing insights on the ecological and evolutionary consequences of developmental plasticity on ageing.

Benefit of cardiac resynchronization therapy among older patients: A patient-level meta-analysis

Cardiac resynchronization therapy (CRT) reduces heart failure hospitalizations (HFH) and mortality for guideline-indicated patients with heart failure (HF). Most patients with HF are aged ≥70 years but such patients are often under-represented in randomized trials. Patient-level data were combined from 8 randomized trials published 2002-2013 comparing CRT to no CRT (n=6,369). The effect of CRT was estimated using an adjusted Bayesian survival model. Using age as a categorical (<70 vs ≥70 years) or continuous variable, the interaction between age and CRT on the composite end point of HFH or all-cause mortality or all-cause mortality alone was assessed. The median age was 67 years with 2436 (38%) being 70+; 1,554 (24%) were women; 2,586 (41%) had nonischemic cardiomyopathy and median QRS duration was 160 ms. Overall, CRT was associated with a delay in time to the composite end point (adjusted hazard ratio [aHR] 0.75, 95% credible interval [CI] 0.66-0.85, P=.002) and all-cause mortality alone (aHR of 0.80, 95% CI 0.69-0.96, P=.017). When age was treated as a categorical variable, there was no interaction between age and the effect of CRT for either end point (P > .1). When age was treated as a continuous variable, older patients appeared to obtain greater benefit with CRT for the composite end point (P for interaction=.027) with a similar but nonsignificant trend for mortality (P for interaction=.35). Reductions in HFH and mortality with CRT are as great or greater in appropriately indicated older patients. Age should not be a limiting factor for the provision of CRT.

Bayesian parametric models for survival prediction in medical applications

BackgroundEvidence-based treatment decisions in medicine are made founded on population-level evidence obtained during randomized clinical trials. In an era of personalized medicine, these decisions should be based on the predicted benefit of a treatment on a patient-level. Survival prediction models play a central role as they incorporate the time-to-event and censoring. In medical applications uncertainty is critical especially when treatments differ in their side effect profiles or costs. Additionally, models must be adapted to local populations without diminishing performance and often without the original training data available due to privacy concern. Both points are supported by Bayesian models—yet they are rarely used. The aim of this work is to evaluate Bayesian parametric survival models on public datasets including cardiology, infectious diseases, and oncology.Materials and methodsBayesian parametric survival models based on the Exponential and Weibull distribution were implemented as a Python package. A linear combination and a neural network were used for predicting the parameters of the distributions. A superiority design was used to assess whether Bayesian models are better than commonly used models such as Cox Proportional Hazards, Random Survival Forest, and Neural Network-based Cox Proportional Hazards. In a secondary analysis, overfitting was compared between these models. An equivalence design was used to assess whether the prediction performance of Bayesian models after model updating using Bayes rule is equivalent to retraining on the full dataset.ResultsIn this study, we found that Bayesian parametric survival models perform as good as state-of-the art models while requiring less hyperparameters to be tuned and providing a measure of the uncertainty of the predictions. In addition, these models were less prone to overfitting. Furthermore, we show that updating these models using Bayes rule yields equivalent performance compared to models trained on combined original and new datasets.ConclusionsBayesian parametric survival models are non-inferior to conventional survival models while requiring less hyperparameter tuning, being less prone to overfitting, and allowing model updating using Bayes rule. Further, the Bayesian models provide a measure of the uncertainty on the statistical inference, and, in particular, on the prediction.

Lapse risk modeling in insurance: a Bayesian mixture approach

AbstractThis paper focuses on modeling surrender time for policyholders in the context of life insurance. In this setup, a large lapse rate at the first months of a contract is often observed, with a decrease in this rate after some months. The modeling of the time to cancelation must account for this specific behavior. Another stylized fact is that policies which are not canceled in the study period are considered censored. To account for both censoring and heterogeneous lapse rates, this work assumes a Bayesian survival model with a mixture of regressions. The inference is based on data augmentation allowing for fast computations even for datasets of over millions of clients. Moreover, frequentist point estimation based on Expectation–Maximization algorithm is also presented. An illustrative example emulates a typical behavior for life insurance contracts, and a simulated study investigates the properties of the proposed model. A case study is considered and illustrates the flexibility of our proposed model allowing different specifications of mixture components. In particular, the observed censoring in the insurance context might be up to $50\%$ of the data, which is very unusual for survival models in other fields such as epidemiology. This aspect is exploited in our simulated study.

Bayesian analysis of the Substrate Ablation vs. Antiarrhythmic Drug Therapy for Symptomatic Ventricular Tachycardia trial.

Bayesian analyses can provide additional insights into the results of clinical trials, aiding in the decision-making process. We analysed the Substrate Ablation vs. Antiarrhythmic Drug Therapy for Symptomatic Ventricular Tachycardia (SURVIVE-VT) trial using Bayesian survival models. The SURVIVE-VT trial randomized patients with ischaemic cardiomyopathy and monomorphic ventricular tachycardia (VT) to catheter ablation or antiarrhythmic drugs (AAD) as a first-line strategy. The primary outcome was a composite of cardiovascular death, appropriate implantable cardioverter-defibrillator shocks, unplanned heart failure hospitalizations, or severe treatment-related complications. We used informative, skeptical, and non-informative priors with different probabilities of large effects to compute the posterior distributions using Markov Chain Monte Carlo methods. We calculated the probabilities of hazard ratios (HR) being <1, <0.9, and <0.75, as well as 2-year survival estimates. Of the 144 randomized patients, 71 underwent catheter ablation and 73 received AAD. Regardless of the prior, catheter ablation had a >98% probability of reducing the primary outcome (HR < 1) and a >96% probability of achieving a reduction of >10% (HR < 0.9). The probability of a >25% (HR < 0.75) reduction of treatment-related complications was >90%. Catheter ablation had a high probability (>93%) of reducing incessant/slow undetected VT/electric storm, unplanned hospitalizations for ventricular arrhythmias, and overall cardiovascular admissions > 25%, with absolute differences of 15.2%, 21.2%, and 20.2%, respectively. In patients with ischaemic cardiomyopathy and VT, catheter ablation as a first-line therapy resulted in a high probability of reducing several clinical outcomes compared to AAD. Our study highlights the value of Bayesian analysis in clinical trials and its potential for guiding treatment decisions. ClinicalTrials.gov identifier: NCT03734562.

Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma.

Extrapolating long-term overall survival (OS) from shorter-term clinical trial data is key to health technology assessment in oncology. However, extrapolation using conventional methods is often subject to uncertainty. Using ciltacabtagene autoleucel (cilta-cel), a chimeric antigen receptor T-cell therapy for multiple myeloma, we used a flexible Bayesian approach to demonstrate use of external longer-term data to reduce the uncertainty in long-term extrapolation. The pivotal CARTITUDE-1 trial (NCT03548207) provided the primary efficacy data for cilta-cel, including a 12-month median follow-up snapshot of OS. Longer-term (48-month median follow-up) survival data from the phase I LEGEND-2 study (NCT03090659) were also available. Twelve-month CARTITUDE-1 OS data were extrapolated in two ways: (1) conventional survival models with standard parametric distributions (uninformed), and (2) Bayesian survival models whose shape prior was informed from 48-month LEGEND-2 data. For validation, extrapolations from 12-month CARTITUDE-1 data were compared with observed 28-month CARTITUDE-1 data. Extrapolations of the 12-month CARTITUDE-1 data using conventional uninformed parametric models were highly variable. Using informative priors from the 48-month LEGEND-2 dataset, the ranges of projected OS at different timepoints were consistently narrower. Area differences between the extrapolation curves and the 28-month CARTITUDE-1 data were generally lower in informed Bayesian models, except for the uninformed log-normal model, which had the lowest difference. Informed Bayesian survival models reduced variation of long-term projections and provided similar projections as the uninformed log-normal model. Bayesian models generated a narrower and more plausible range of OS projections from 12-month data that aligned with observed 28-month data. CARTITUDE-1 ClinicalTrials.gov identifier, NCT03548207. LEGEND-2 ClinicalTrials.gov identifier, NCT03090659, registered retrospectively on 27 March 2017, and ChiCTR-ONH-17012285.

Bayesian inference for survival prediction of childhood Leukemia

BackgroundChildhood Leukemia is the most common type of cancer among children. Nearly 39% of cancer-induced childhood deaths are attributable to Leukemia. Nevertheless, early intervention has long been underdeveloped. Moreover, there are still a group of children succumbing to their cancer due to the cancer care resource disparity. Therefore, it calls for an accurate predictive approach to improve childhood Leukemia survival and mitigate these disparities. Existing survival predictions rely on a single best model, which fails to consider model uncertainties in predictions. Prediction from a single model is brittle, with model uncertainty neglected, and inaccurate prediction could lead to serious ethical and economic consequences. MethodsTo address these challenges, we develop a Bayesian survival model to predict patient-specific survivals by taking model uncertainty into account. Specifically, we first develop a survival model predict time-varying survival probabilities. Second, we place different prior distributions over various model parameters and estimate their posterior distribution with full Bayesian inference. Third, we predict the patient-specific survival probabilities changing with respect to time by considering model uncertainty induced by posterior distribution. ResultsConcordance index of the proposed model is 0.93. Moreover, the standardized survival probability of the censored group is higher than that of the deceased group. ConclusionsExperimental results indicate that the proposed model is robust and accurate in predicting patient-specific survivals. It can also help clinicians track the contribution of multiple clinical attributes, thereby enabling well-informed intervention and timely medical care for childhood Leukemia.

Assessing survival time of outpatients with cervical cancer: at the university of Gondar referral hospital using the Bayesian approach

BackgroundCervical cancer is the 4th most common cancer in women worldwide. as well as the 4th most common cause of cancer-related death. The main objective of this study was to identify factors that affect the survival time of outpatients with cervical cancer.MethodsA retrolective study including outpatients with cervical cancer was carried out in a hospital. To achieve the aim, 322 outpatients with cervical cancer were included in the study based on the data taken from the medical records of patients enrolled from May 15, 2018, to May 15, 2022, at the University of Gondar referral hospital, Gondar, Ethiopia. The Kaplan–Meier plots and log-rank test were used for the comparison of survival functions; the Cox-PH model and Bayesian parametric survival models were used to analyze the survival times of outpatients with cervical cancer. Integrated nested Laplace approximation methods have been applied.ResultsOut of a total of 322 patients, 118 (36.6%) died as outpatients. The estimated median survival time for patients was 42 months. Using model selection criteria, the Bayesian log-normal accelerated failure time model was found to be appropriate. According to the results of this model, oral contraceptive use, HIV, stage, grade, co-morbid disease, history of abortion, weight, histology type, FIGO stage, radiation, chemotherapy, LVSI, metastatic number, regional nodes examined, and tumor size all have a significant impact on the survival time of outpatients with cervical cancer. The Bayesian log-normal accelerated failure time model accurately predicted the survival time of cervical cancer outpatients.ConclusionsThe findings of this study suggested that reductions in weight, treatment, the presence of comorbid disease, the presence of HIV, squamous cell histology type, having a history of abortion, oral contraceptive use, a large tumor size, an increase in the International Federation of Gynecologists and Obstetricians stage, an increase in metastasis number, an increase in grade, positive regional nodes, lymphatic vascular space invasion, and late stages of cancer all shortened the survival time of cervical cancer outpatients.

Modified Bayesian survival analysis of Diabetes Mellitus in selected hospital facilities in Nasarawa, Nigeria

Diabetes mellitus is a global chronic health problem affecting over 400 million people. The study focused on the commonest type of Diabetes-Type II diabetes. The disease is associated with morbidity and mortality. Bayesian survival model may be utilized to assess the risk factors associated with Diabetes. The study utilized secondary data from 532 diabetic patients from two General Hospital facilities in Nasarawa State, Nigeria. The aim of the paper was to apply a Bayesian survival model on diabetic dataset to assess some risk factors pertaining to the disease. This Bayesian model was modified to Diabetic Additive Models (DAMS) and further extended to the Diabetic Additive Constant Hazard Model (DACHM), the coded version C. DACHM (when all metrical covariates were coded) and Diabetic Additive Accelerated Failure Time Model (DAAFTM). The results show that C.DACHM outperforms the other model with least values of Watanabe Akaike Information Criterion (WAIC), Deviance Information Criterion (DIC), and a large predictive power measured by the Log Pseudo Maximum Likelihood (LPML). The C.DACH model suggests that; good management of type II diabetes patients aged 40 years and above in both hospitals reduced the risk of death. Considerably, low Body Mass Index (BMI) increased the risk of death of patients with the disease. Body Mass Index, BMI greater than 24.9 (overweight) are 5.41E-17 times at risk of death from diabetes than those of normal weight. High Systolic Blood Pressure, SBP, greater than 140 (high) increases the risk of dying from the diseases by 1.51 times than those of normal SBP. High Diastolic Blood Pressure, DBP, greater than or equal to 90 (high) increases the risk of dying from the diseases by 7.81 times than those of normal DBP. Male patients were 1.28 times at risk of death from diabetes than their female patients. Patients of General Hospital Keffi experience are 1.02 times at risk of death than those of the General Hospital Nasarawa. The research recommends patients’ drug compliance especially for patients above 40 years, maintenance of a healthy body mass index and maintenance of a healthy blood pressure.

Survival of Common Loon chicks appears unaffected by Bald Eagle recovery in northern Minnesota

Recovering species are not returning to the same environments or communities from which they disappeared. Conservation researchers and practitioners are thus faced with additional challenges in ensuring species resilience in these rapidly changing ecosystems. Assessing the resilience of species in these novel systems can still be guided by species’ ecology, including knowledge of their population size, life history traits, and behavioral adaptations, as well as the type, strength, and number of ways that they interact with other species in the community. We summarized broad trends of Common Loons (<em>Gavia immer</em>) breeding at Voyageurs National Park from 1973 to 2009, and evaluated the effects of increased risk from recovering Bald Eagles (<em>Haliaeetus leucocephalus</em>) on chick survival from 2004 to 2006. Adult Common Loons appear to have increased over time. Using Bayesian survival models that accounted for imperfect detection of unmarked individuals, we determined that chick survival of Common Loons was high from year to year and was unrelated to predation risk from Bald Eagles because chicks in territories closer to active nests did not experience greater mortality than those farther away. We suggest that Common Loon chicks were unaffected by the recovery of this top predator during the three years of sampling. Previous research indicates that Bald Eagles and other predators are an important source of egg losses, but Common Loons can compensate by re-nesting. Despite current uncertainties from anthropogenic threats, knowledge of a species’ ecology remains instrumental in determining its resilience during recovery.

Associating Incident Clearance Duration with Freeway Segment Types Using Hierarchical Bayesian Survival Model

Associating Incident Clearance Duration with Freeway Segment Types Using Hierarchical Bayesian Survival Model

Effect of preservation of antegrade hypogastric flow on development of claudication following aortoiliac aneurysm repair

ObjectiveThe objective of this study was to compare the rate of development of buttock claudication in patients undergoing aortoiliac aneurysm repair with and without exclusion of antegrade hypogastric arterial flow. In the absence of convincing data, questions remain regarding the best management of hypogastric arterial flow to prevent the theoretical risk of buttock claudication. MethodsThe Veterans’ Affairs Open Versus Endovascular Repair (OVER) Cooperative Study prospectively collected information on buttock claudication. Trial participants were specifically prompted both pre- and postoperatively to report the development of claudication symptoms at several anatomic levels. Of note, trial investigators were specifically trained to occlude the trunk hypogastric arterial, preserving the anterior and posterior divisions. Bayesian survival models were created to evaluate time to development of left, right, or bilateral buttock claudication according to the presence/absence of antegrade hypogastric perfusion. ResultsA total of 881 patients from the OVER trial with information regarding status of hypogastric flow were included in the analysis. Of these, 788 patients maintained bilateral antegrade hypogastric arterial perfusion, 63 had right hypogastric coverage/occlusion, and 27 had left hypogastric coverage/occlusion, whereas 3 patients had bilateral hypogastric coverage/occlusion. Just under 5% of all patients (n = 41) developed buttock claudication. After adjustment for smoking, chronic obstructive pulmonary disease, medications, study arm, preoperative activity level, body mass index, age, and diabetes, intervention-related changes to hypogastric perfusion had no effect on time to development of buttock claudication. A Maximum A Posteriori Kullback- Leibler misfit χ2 was 14.45 with 24 degrees of freedom, resulting in a goodness of fit P-value of P = .94, indicative of a good fit. ConclusionsOVER is the largest aneurysm treatment study to prospectively collect data related to the development of claudication as well as hypogastric preservation status. Despite this, we were unable to find evidence to support the assertion that preservation of antegrade hypogastric flow decreases the rate of development of buttock claudication symptoms. The low rate of development of buttock claudication overall and in the subgroups is striking.

Overweight or obesity increases the risk of cardiovascular disease among older Australian adults, even in the absence of cardiometabolic risk factors: a Bayesian survival analysis from the Hunter Community Study.

To estimate the risk of cardiovascular disease (CVD) in older adults with overweight or obesity without metabolic risk factors using a Bayesian survival analysis. Prospective cohort study with median follow-up of 9.7 years. Newcastle, New South Wales, Australia. A total of 2313 community-dwelling older men and women. Participants without known CVD and with a body mass index (BMI) ≥ 18.5 kg m2 were stratified by BMI and metabolic risk to create six BMI-metabolic health categories. Metabolic risk was defined according to the International Diabetes Federation criteria for metabolic syndrome. 'Metabolically healthy' was defined as absence of metabolic risk factors. Bayesian survival analysis, incorporating prior information from a previously published meta-analysis was used to assess the effect of BMI-metabolic health categories on time from recruitment to CVD. Incident physician-diagnosed CVD, defined as fatal or nonfatal myocardial infarction, fatal or nonfatal stroke, angina, or coronary revascularisation procedure, was determined by linkage to hospital admissions records and Medicare Australia data. Secondary outcomes were cardiovascular mortality and all-cause mortality. From 2313 adults with complete metabolic health data over a median follow-up of 9.7 years, 283 incident CVD events, 58 CVD related deaths and 277 deaths from any cause occurred. In an adjusted Bayesian survival model of complete cases with informative prior and metabolically healthy normal weight as the reference group, the risk of CVD was increased in metabolically healthy overweight (HR = 1.52, 95% credible interval 0.96-2.36), and in metabolically healthy obesity (HR = 1.86, 95% credible interval 1.14-3.08). Imputation of missing metabolic health and confounding data did not change the results. There was increased risk of CVD in older adults with overweight or obesity, even in the absence of any metabolic abnormality. This argues against the notion of 'metabolically healthy' overweight or obesity.