Bayesian Spatial Survival 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.

Spatial variation and disparity in female breast cancer relative survival in the United States.

Breast cancer is the most common cancer among women in the United States. However, data on spatial disparities in survival for breast cancer are limited in the country. This study estimated 5-year relative survival (RS) of female breast cancer and examined the spatial variations across the contiguous United States. Women newly diagnosed with breast cancer in 2003-2010 in the United States were identified from the National Cancer Database and followed up through 2016. The crude 5-year RS at the county level was estimated and adjusted for patients' key sociodemographic and clinical factors. To account for spatial effects, the RS estimates were smoothed using the Bayesian spatial survival model. A local spatial autocorrelation analysis with the Getis-Ord Gi* statistics was applied to identify geographic clusters of low or high RS. Clusters of low RS were identified in more than 15 states covering 671 counties, mostly in the southeast and southwest regions, including Georgia, Alabama, Mississippi, Louisiana, Arkansas, Oklahoma, and Texas. Approximately 30% of these clusters can be explained by patients' characteristics: Race, insurance, and stage at diagnosis appeared to be the major attributable factors. Significant spatial disparity in female breast cancer survival was found, with low RS clusters identified in Georgia, Alabama, Mississippi, Louisiana, Arkansas, Oklahoma, and Texas. Policies and interventions that focus on serving Black women, improvements in insurance coverage, and early detection in these areas could potentially mitigate the spatial disparities.

Bayesian spatial survival modelling for dengue fever in Makassar, Indonesia

ObjectiveTo understand the spatial pattern of dengue fever (DF) patients’ survival and investigated factors influencing DF patients’ survival. MethodA Bayesian spatial survival method via a conditional autoregressive approach was used to analyze the factors that influence DF patients’ survival in 14 sub-districts from January 2015 to May 2017 in Makassar city, Indonesia. Bayesian spatial and a non-spatial model were compared by using deviance information criterion. ResultsThe spatial model was more suitable than a non-spatial model. Under the Bayesian spatial model, there was a substantive relationship between age, grade and DF patients’ survival time. ConclusionsThe relative risk map and related factors of DF patients’ survival can indicate the health policy makers to give special attention to the high risk areas in order to faster and more targeted treatment.

Bayesian Spatial Survival Analysis of Duration to Cure among New Smear-Positive Pulmonary Tuberculosis (PTB) Patients in Iran, during 2011-2018.

Mycobacterium tuberculosis is the causative agent of tuberculosis (TB), and pulmonary TB is the most prevalent form of the disease worldwide. One of the most concrete actions to ensure an effective TB control program is monitoring TB treatment outcomes, particularly duration to cure; but, there is no strong evidence in this respect. Thus, the primary aim of this study was to examine the possible spatial variations of duration to cure and its associated factors in Iran using the Bayesian spatial survival model. All new smear-positive PTB patients have diagnosed from March 2011 to March 2018 were included in the study. Out of 34,744 patients, 27,752 (79.90%) patients cured and 6992 (20.10%) cases were censored. For inferential purposes, the Markov chain Monte Carlo algorithms are applied in a Bayesian framework. According to the Bayesian estimates of the regression parameters in the proposed model, a Bayesian spatial log-logistic model, the variables gender (male vs. female, TR = 1.09), altitude (>750 m vs. ≤750 m, TR = 1.05), bacilli density in initial smear (3+ and 2+ vs. 1–9 Basil & 1+, TR = 1.09 and TR = 1.02, respectively), delayed diagnosis (>3 months vs. <1 month, TR = 1.02), nationality (Iranian vs. other, TR = 1.02), and location (urban vs. rural, TR = 1.02) had a significant influence on prolonging the duration to cure. Indeed, pretreatment weight (TR = 0.99) was substantially associated with shorter duration to cure. In summary, the spatial log-logistic model with convolution prior represented a better performance to analyze the duration to cure of PTB patients. Also, our results provide valuable information on critical determinants of duration to cure. Prolonged duration to cure was observed in provinces with low TB incidence and high average altitude as well. Accordingly, it is essential to pay a special attention to such provinces and monitor them carefully to reduce the duration to cure while maintaining a focus on high-risk provinces in terms of TB prevalence.

Association of ambient air pollution with cardiovascular disease risks in people with type 2 diabetes: a Bayesian spatial survival analysis

BackgroundEvidence is limited on excess risks of cardiovascular diseases (CVDs) associated with ambient air pollution in diabetic populations. Survival analyses without considering the spatial structure and possible spatial correlations in health and environmental data may affect the precision of estimation of adverse environmental pollution effects. We assessed the association between air pollution and CVDs in type 2 diabetes through a Bayesian spatial survival approach.MethodsTaiwan’s national-level health claims and air pollution databases were utilized. Fine individual-level latitude and longitude were used to determine pollution exposure. The exponential spatial correlation between air pollution and CVDs was analyzed in our Bayesian model compared to traditional Weibull and Cox models.ResultsThere were 2072 diabetic patients included in analyses. PM2.5 and SO2 were significant CVD risk factors in our Bayesian model, but such associations were attenuated or underestimated in traditional models; adjusted hazard ratio (HR) and 95% credible interval (CrI) or confidence interval (CI) of CVDs for a 1 μg/m3 increase in the monthly PM2.5 concentration for our model, the Weibull and Cox models was 1.040 (1.004–1.073), 0.994 (0.984–1.004), and 0.994 (0.984–1.004), respectively. With a 1 ppb increase in the monthly SO2 concentration, adjusted HR (95% CrI or CI) was 1.886 (1.642–2.113), 1.092 (1.022–1.168), and 1.091 (1.021–1.166) for these models, respectively.ConclusionsAgainst traditional non-spatial analyses, our Bayesian spatial survival model enhances the assessment precision for environmental research with spatial survival data to reveal significant adverse cardiovascular effects of air pollution among vulnerable diabetic patients.Graphical abstract

Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia.

Spatial models are becoming more popular in time-to-event data analysis. Commonly, the intrinsic conditional autoregressive prior is placed on an area level frailty term to allow for correlation between areas. We considered a range of Bayesian Weibull and Cox semiparametric spatial models to describe a dataset on hospitalisation of dengue. This paper aimed to extend these two models, to evaluate the suitability of these models for estimation and prediction of the length of stay, compare different spatial priors, and determine factors that significantly affect the duration of hospital stay for dengue fever patients in the case study location, namely Wahidin hospital in Makassar, Indonesia. We compared two different models with three different spatial priors with respect to goodness of fit and generalisability. For all models considered, the Leroux prior was preferred over the intrinsic conditional autoregressive and independent priors, but Cox and Weibull versions had similar predictive performance, model fit, and results. Age and platelet count were negatively associated with the length of stay, while red blood cell count was positively associated with the length of stay of dengue patients at this hospital. Using appropriate Bayesian spatial survival models enables identification of factors that substantively affect the length of stay.

SpBayesSurv: Fitting Bayesian Spatial Survival Models Using R

Spatial survival analysis has received a great deal of attention over the last 20 years due to the important role that geographical information can play in predicting survival. This paper provides an introduction to a set of programs for implementing some Bayesian spatial survival models in R using the package spBayesSurv. The function survregbayes includes the three most commonly-used semiparametric models: proportional hazards, proportional odds, and accelerated failure time. All manner of censored survival times are simultaneously accommodated including uncensored, interval censored, current-status, left and right censored, and mixtures of these. Left-truncated data are also accommodated. Time-dependent covariates are allowed under the piecewise constant assumption. Both georeferenced and areally observed spatial locations are handled via frailties. Model fit is assessed with conditional Cox-Snell residual plots, and model choice is carried out via the log pseudo marginal likelihood, the deviance information criterion and the Watanabe-Akaike information criterion. The accelerated failure time frailty model with a covariate-dependent baseline is included in the function frailtyGAFT. In addition, the package also provides two marginal survival models: proportional hazards and linear dependent Dirichlet process mixture, where the spatial dependence is modeled via spatial copulas. Note that the package can also handle non-spatial data using non-spatial versions of aforementioned models.

Bayesian spatial survival models for HIV/AIDS event processes in East Java

Bayesian spatial survival models for HIV/AIDS event processes in East Java

Landscape and climate determine patterns of spread for all colour morphs of the alien ladybird Harmonia axyridis

AbstractAimUnderstanding why some areas are more vulnerable to invasion by invasive alien species than others represents a key challenge in invasion biology. We investigated the roles of landscape, climate, proximity to initial invasion sites, recording intensity and colour pattern polymorphism in explaining the spread of the invasive ladybird Harmonia axyridis across Great Britain.LocationThe British mainland and inshore islands (Anglesey, Isle of Wight and the Inner Hebrides).MethodsPatterns in time to invasion of H. axyridis were quantified at a hectad level (10 km grid squares) from over 23,000 records. Bayesian spatial survival models were developed relating spread patterns to concurrent patterns in climate, land cover and recording intensity. Separate models were developed for melanic (f. conspicua and f. spectabilis) and non‐melanic (f. succinea) colour forms that were hypothesized to differ in their response to climate factors, particularly temperature, and subsequent spread.ResultsHarmonia axyridis spread more rapidly into hectads containing a high proportion of urban land cover and marginally more slowly into hectads containing high conifer cover. Invasions were recorded more rapidly in hectads with high recording intensity, or that were close to initial invasion sites. Harmonia axyridis colour morphs spread to similar extents, although increased sunshine significantly enhanced the spread of the non‐melanic form (f. succinea). Correspondence between observed and predicted times of invasion from survival models was substantial (Pearson's rp for all H. axyridis colour forms = 0.834).Main conclusionsThe ability of H. axyridis to use a wide range of habitats and prey species and overwinter inside buildings may have allowed this species to establish rapidly in urban areas. Native ladybird species in urban areas may suffer combined negative impacts of habitat deterioration and competition from H. axyridis. Spatial survival models help us to identify areas vulnerable to invasion and to understand the impacts of multiple environmental drivers on the spread of invasive alien species, given detailed data on time to invasion.

Bayesian Spatial Survival Models for Political Event Processes

Research in political science is increasingly, but independently, modeling heterogeneity and spatial dependence. This article draws together these two research agendas via spatial random effects survival models. In contrast to standard survival models, which assume spatial independence, spatial survival models allow for spatial autocorrelation at neighboring locations. I examine spatial dependence in both semiparametric Cox and parametric Weibull models and in both individual and shared frailty models. I employ a Bayesian approach in which spatial autocorrelation in unmeasured risk factors across neighboring units is incorporated via a conditionally autoregressive (CAR) prior. I apply the Bayesian spatial survival modeling approach to the timing of U.S. House members' position announcements on NAFTA. I find that spatial shared frailty models outperform standard nonfrailty models and nonspatial frailty models in both the semiparametric and parametric analyses. The modeling of spatial dependence also produces changes in the effects of substantive covariates in the analysis.