Identifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty.

Motivated by the implementation of a SARS-Cov-2 sewer surveillance system in Chile during the COVID-19 pandemic, we propose a set of mathematical and algorithmic tools that aim to identify the location of an outbreak under uncertainty in the network structure. Given an upper bound on the number of samples we can take on any given day, our framework allows us to detect an unknown infected node by adaptively sampling different network nodes on different days. Crucially, despite the uncertainty of the network, the method allows univocal detection of the infected node, albeit at an extra cost in time. This framework relies on a specific and well-chosen strategy that defines new nodes to test sequentially, with a heuristic that balances the granularity of the information obtained from the samples. We extensively tested our model in real and synthetic networks, showing that the uncertainty of the underlying graph only incurs a limited increase in the number of iterations, indicating that the methodology is applicable in practice.

Effectiveness of the second COVID-19 booster against Omicron: a large-scale cohort study in Chile.

In light of the ongoing COVID-19 pandemic and the emergence of new SARS-CoV-2 variants, understanding the effectiveness of various booster vaccination regimens is pivotal. In Chile, using a prospective national cohort of 3.75 million individuals aged 20 or older, we evaluate the effectiveness against COVID-19-related intensive care unit (ICU) admissions and death of mRNA-based second vaccine boosters for four different three-dose background regimes: BNT162b2 primary series followed by a homologous booster, and CoronaVac primary series followed by an mRNA booster, a homologous booster, and a ChAdOx-1 booster. We estimate the vaccine effectiveness weekly from February 14 to August 15, 2022, by determining hazard ratios of immunization over non-vaccination, accounting for relevant confounders. The overall adjusted effectiveness of a second mRNA booster shot is 88.2% (95%CI, 86.2-89.9) against ICU admissions and 90.5% (95%CI 89.4-91.4) against death. Vaccine effectiveness shows a mild decrease for all regimens and outcomes, probably linked to the introduction of BA.4 and BA.5 Omicron sub-lineages and the waning of immunity. Based on our findings, individuals might not need additional boosters for at least 6 months after receiving a second mRNA booster shot in this setting.

High Burden of Intestinal Colonization With Antimicrobial-Resistant Bacteria in Chile: An Antibiotic Resistance in Communities and Hospitals (ARCH) Study.

BACKGROUND: Antimicrobial resistance is a global threat, heavily impacting low- and middle-income countries. This study estimated antimicrobial-resistant gram-negative bacteria (GNB) fecal colonization prevalence in hospitalized and community-dwelling adults in Chile before the coronavirus disease 2019 pandemic. METHODS: From December 2018 to May 2019, we enrolled hospitalized adults in 4 public hospitals and community dwellers from central Chile, who provided fecal specimens and epidemiological information. Samples were plated onto MacConkey agar with ciprofloxacin or ceftazidime added. All recovered morphotypes were identified and characterized according to the following phenotypes: fluoroquinolone-resistant (FQR), extended-spectrum cephalosporin-resistant (ESCR), carbapenem-resistant (CR), or multidrug-resistant (MDR; as per Centers for Disease Control and Prevention criteria) GNB. Categories were not mutually exclusive. RESULTS: A total of 775 hospitalized adults and 357 community dwellers were enrolled. Among hospitalized subjects, the prevalence of colonization with FQR, ESCR, CR, or MDR-GNB was 46.4% (95% confidence interval [CI], 42.9-50.0), 41.2% (95% CI, 37.7-44.6), 14.5% (95% CI, 12.0-16.9), and 26.3% (95% CI, 23.2-29.4). In the community, the prevalence of FQR, ESCR, CR, and MDR-GNB colonization was 39.5% (95% CI, 34.4-44.6), 28.9% (95% CI, 24.2-33.6), 5.6% (95% CI, 3.2-8.0), and 4.8% (95% CI, 2.6-7.0), respectively. CONCLUSIONS: A high burden of antimicrobial-resistant GNB colonization was observed in this sample of hospitalized and community-dwelling adults, suggesting that the community is a relevant source of antibiotic resistance. Efforts are needed to understand the relatedness between resistant strains circulating in the community and hospitals.

Effectiveness of an inactivated SARS-CoV-2 vaccine in children and adolescents: a large-scale observational study.

Background: Policymakers urgently need evidence to adequately balance the costs and benefits of mass vaccination against COVID-19 across all age groups, including children and adolescents. In this study, we aim to assess the effectiveness of CoronaVac's primary series among children and adolescents in Chile. Methods: We used a large prospective national cohort of about two million children and adolescents 6-16 years to estimate the effectiveness of an inactivated SARS-CoV-2 vaccine (CoronaVac) in preventing laboratory-confirmed symptomatic SARS-CoV-2 infection (COVID-19), hospitalisation, and admission to an intensive care unit (ICU) associated with COVID-19. We compared the risk of individuals treated with a complete primary immunization schedule (two doses, 28 days apart) with the risk of unvaccinated individuals during the follow-up period. The study was conducted in Chile from June 27, 2021, to January 12, 2022, when the SARS-CoV-2 Delta variant was predominant but other variants of concern were co-circulating, including Omicron. We used inverse probability-weighted survival regression models to estimate hazard ratios of complete immunization over the unvaccinated status, accounting for time-varying vaccination exposure and adjusting for relevant demographic, socioeconomic, and clinical confounders. Findings: The estimated adjusted vaccine effectiveness for the inactivated SARS-CoV-2 vaccine in children aged 6-16 years was 74.5% (95% CI, 73.8-75.2), 91.0% (95% CI, 87.8-93.4), 93.8% (95% CI, 87.8-93.4) for the prevention of COVID-19, hospitalisation, and ICU admission, respectively. For the subgroup of children 6-11 years, the vaccine effectiveness was 75.8% (95% CI, 74.7-76.8) for the prevention of COVID-19 and 77.9% (95% CI, 61.5-87.3) for the prevention of hospitalisation. Interpretation: Our results suggest that a complete primary immunization schedule with the inactivated SARS-CoV-2 vaccine provides effective protection against severe COVID-19 disease for children 6-16 years. Funding: Agencia Nacional de Investigación y Desarrollo (ANID) Millennium Science Initiative Program and Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias (FONDAP).

Quantitative Susceptibility Mapping MRI in Deep-Brain Nuclei in First-Episode Psychosis.

BACKGROUND: Psychosis is related to neurochemical changes in deep-brain nuclei, particularly suggesting dopamine dysfunctions. We used an magnetic resonance imaging-based technique called quantitative susceptibility mapping (QSM) to study these regions in psychosis. QSM quantifies magnetic susceptibility in the brain, which is associated with iron concentrations. Since iron is a cofactor in dopamine pathways and co-localizes with inhibitory neurons, differences in QSM could reflect changes in these processes. METHODS: We scanned 83 patients with first-episode psychosis and 64 healthy subjects. We reassessed 22 patients and 21 control subjects after 3 months. Mean susceptibility was measured in 6 deep-brain nuclei. Using linear mixed models, we analyzed the effect of case-control differences, region, age, gender, volume, framewise displacement (FD), treatment duration, dose, laterality, session, and psychotic symptoms on QSM. RESULTS: Patients showed a significant susceptibility reduction in the putamen and globus pallidus externa (GPe). Patients also showed a significant R2* reduction in GPe. Age, gender, FD, session, group, and region are significant predictor variables for QSM. Dose, treatment duration, and volume were not predictor variables of QSM. CONCLUSIONS: Reduction in QSM and R2* suggests a decreased iron concentration in the GPe of patients. Susceptibility reduction in putamen cannot be associated with iron changes. Since changes observed in putamen and GPe were not associated with symptoms, dose, and treatment duration, we hypothesize that susceptibility may be a trait marker rather than a state marker, but this must be verified with long-term studies.

Neutralizing antibodies induced by homologous and heterologous boosters in CoronaVac vaccines in Chile.

OBJECTIVES: To determine the impact of a booster dose on the humoral response in individuals inoculated with a complete schedule of any SARS-CoV-2 vaccine, we evaluated the neutralizing antibody (NAb) titres of homologous or heterologous booster doses over a 90-days period in CoronaVac vaccinees from 3 centres in Santiago, Chile. METHODS: Individuals previously inoculated with 2 doses of CoronaVac (N = 523) were recruited in the context of the REFUERZO clinical trial (NCT04992182) and received either placebo (N = 129), or a booster dose of CoronaVac (N = 134), BNT162b2 (N = 133), or ChAdOx1 (N = 127). Pseudovirus neutralizing antibody titres (pVNT) were determined at baseline (day 0) as well as at days 14, 30, 60, and 90 after booster dose administration. RESULTS: Inoculating a booster dose increases the pVNTs titres at days 14 and 30 in all groups, (13.5- and 12.0-fold increase for the CoronaVac group; 247.0- and 212.3-fold increase for the BTN162b2 group; and 89.1- and 128.1-fold increase for ChAdOx1 at each time point, respectively) with a decline observed at days 60 and 90. However, although pVNTs remained significantly higher for the BTN162b2 and ChAdOx1 groups at days 60 and 90, NAb titres reached baseline levels in the CoronaVac group at 90 days after inoculation. DISCUSSION: A single heterologous booster (BTN162b2 or ChAdOx1) in individuals who completed the CoronaVac primary series resulted in an important increase in NAb titres remaining significantly higher at least for 90 days. These data may directly impact middle- and low-income countries currently using CoronaVac as the main vaccination strategy.

Primary esophageal diffuse large B-cell lymphoma.

A 76-year-old man presented with dysphagia, epigastric pain and weight loss for the last two months. Heavy sweating was also presented. Past medical conditions included type 2 diabetes. He had no evidence of any immunosupressive disease including HIV infection. Physical examination only revealed low-grade fever. Laboratory data showed leukocytosis. Gastroscopy evidenced a complete esophageal stenosis starting at 30 cm, with a severely friable mucosa of malignant appearance. The results of biopsies were insufficient for diagnosis of malignancy. Computed tomography demonstrated a 10-cm irregular tumor located in the distal and middle thirds of the esophagus, which resulted in narrowing of the lumen. Involving tracheal carina, bronchus, and descending aorta were observed. Perforation signs were also seen. Distant metastases were not found. Empirical treatment with piperacillin/tazobactan was started. A surgical gastrostomy to allows nutritional support was performed. Two other gastroscopies were performed resulting in an inconclusive diagnosis. Finally, flow cytometry performed in samples obtained by endobronchial ultrasound-guided biopsy evidenced prominent clonal B-cell populations consistent with extranodal diffuse large B-cell lymphoma exhibing CD10 expression. A treatment with Rituximab in combination with cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) was started. Primary esophageal diffuse large B-cell lymphoma (DLBCL), a variant of non-Hodgkin's lymphoma, accounts for less than 1% of all cases of gastrointestinal lymphomas.

Effectiveness of CoronaVac in children 3-5 years of age during the SARS-CoV-2 Omicron outbreak in Chile.

The outbreak of the B.1.1.529 lineage of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Omicron) has caused an unprecedented number of Coronavirus Disease 2019 (COVID-19) cases, including pediatric hospital admissions. Policymakers urgently need evidence of vaccine effectiveness in children to balance the costs and benefits of vaccination campaigns, but, to date, the evidence is sparse. Leveraging a population-based cohort in Chile of 490,694 children aged 3-5 years, we estimated the effectiveness of administering a two-dose schedule, 28 days apart, of Sinovac's inactivated SARS-CoV-2 vaccine (CoronaVac). We used inverse probability-weighted survival regression models to estimate hazard ratios of symptomatic COVID-19, hospitalization and admission to an intensive care unit (ICU) for children with complete immunization over non-vaccination, accounting for time-varying vaccination exposure and relevant confounders. The study was conducted between 6 December 2021 and 26 February 2022, during the Omicron outbreak in Chile. The estimated vaccine effectiveness was 38.2% (95% confidence interval (CI), 36.5-39.9) against symptomatic COVID-19, 64.6% (95% CI, 49.6-75.2) against hospitalization and 69.0% (95% CI, 18.6-88.2) against ICU admission. The effectiveness against symptomatic COVID-19 was modest; however, protection against severe disease was high. These results support vaccination of children aged 3-5 years to prevent severe illness and associated complications and highlight the importance of maintaining layered protections against SARS-CoV-2 infection.

Effectiveness of homologous and heterologous booster doses for an inactivated SARS-CoV-2 vaccine: a large-scale prospective cohort study.

BACKGROUND: Several countries have authorised or begun using a booster vaccine dose against COVID-19. Policy makers urgently need evidence of the effectiveness of additional vaccine doses and its clinical spectrum for individuals with complete primary immunisation schedules, particularly in countries where the primary schedule used inactivated SARS-CoV-2 vaccines. METHODS: Using individual-level data, we evaluated a prospective, observational, national-level cohort of individuals (aged ≥16 years) affiliated with the Fondo Nacional de Salud insurance programme in Chile, to assess the effectiveness of CoronaVac (Sinovac Biotech), AZD1222 (Oxford-AstraZeneca), or BNT162b2 (Pfizer-BioNTech) vaccine boosters in individuals who had completed a primary immunisation schedule with CoronaVac, compared with unvaccinated individuals. Individuals administered vaccines from Feb 2, 2021, to the prespecified study end date of Nov 10, 2021, were evaluated; we excluded individuals with a probable or confirmed SARS-CoV-2 infection (RT-PCR or antigen test) on or before Feb 2, 2021, and individuals who had received at least one dose of any COVID-19 vaccine before Feb 2, 2021. We estimated the vaccine effectiveness of booster doses against laboratory-confirmed symptomatic COVID-19 (symptomatic COVID-19) cases and COVID-19 outcomes (hospitalisation, admission to the intensive care unit [ICU], and death We used inverse probability-weighted and stratified survival regression models to estimate hazard ratios, accounting for time-varying vaccination status and adjusting for relevant demographic, socioeconomic, and clinical confounders. We estimated the change in hazard from unvaccinated status to vaccinated status associated with the primary immunisation series and a booster vaccine. FINDINGS: 11 174 257 individuals were eligible for this study, among whom 4 127 546 completed a primary immunisation schedule (two doses) with CoronaVac and received a booster dose during the study period. 1 921 340 (46·5%) participants received an AZD1222 booster, 2 019 260 (48·9%) received a BNT162b2 booster, and 186 946 (4·5%) received a homologous booster with CoronaVac. We calculated an adjusted vaccine effectiveness (weighted stratified Cox model) in preventing symptomatic COVID-19 of 78·8% (95% CI 76·8-80·6) for a three-dose schedule with CoronaVac, 96·5% (96·2-96·7) for a BNT162b2 booster, and 93·2% (92·9-93·6) for an AZD1222 booster. The adjusted vaccine effectiveness against COVID-19-related hospitalisation, ICU admission, and death was 86·3% (83·7-88·5), 92·2% (88·7-94·6), and 86·7% (80·5-91·0) for a homologous CoronaVac booster, 96·1% (95·3-96·9), 96·2% (94·6-97·3), and 96·8% (93·9-98·3) for a BNT162b2 booster, and 97·7% (97·3-98·0), 98·9% (98·5-99·2), and 98·1% (97·3-98·6) for an AZD1222 booster. INTERPRETATION: Our results suggest that a homologous or heterologous booster dose for individuals with a complete primary vaccination schedule with CoronaVac provides a high level of protection against COVID-19, including severe disease and death. Heterologous boosters showed higher vaccine effectiveness than a homologous booster for all outcomes, providing additional support for a mix-and-match approach. FUNDING: Agencia Nacional de Investigación y Desarrollo through the Fondo Nacional de Desarrollo Científico y Tecnológico, Millennium Science Initiative Program, and Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias.

The role of body mass index at diagnosis of colorectal cancer on Black-White disparities in survival: a density regression mediation approach.

The study of racial/ethnic inequalities in health is important to reduce the uneven burden of disease. In the case of colorectal cancer (CRC), disparities in survival among non-Hispanic Whites and Blacks are well documented, and mechanisms leading to these disparities need to be studied formally. It has also been established that body mass index (BMI) is a risk factor for developing CRC, and recent literature shows BMI at diagnosis of CRC is associated with survival. Since BMI varies by racial/ethnic group, a question that arises is whether differences in BMI are partially responsible for observed racial/ethnic disparities in survival for CRC patients. This article presents new methodology to quantify the impact of the hypothetical intervention that matches the BMI distribution in the Black population to a potentially complex distributional form observed in the White population on racial/ethnic disparities in survival. Our density mediation approach can be utilized to estimate natural direct and indirect effects in the general causal mediation setting under stronger assumptions. We perform a simulation study that shows our proposed Bayesian density regression approach performs as well as or better than current methodology allowing for a shift in the mean of the distribution only, and that standard practice of categorizing BMI leads to large biases when BMI is a mediator variable. When applied to motivating data from the Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium, our approach suggests the proposed intervention is potentially beneficial for elderly and low-income Black patients, yet harmful for young or high-income Black populations.

Tool for Estimating the Probability of Having COVID-19 With 1 or More Negative RT-PCR Results.

Early case detection and isolation of infected individuals are critical to controlling coronavirus disease 2019 (COVID-19). Reverse transcription polymerase chain reaction (RT-PCR) is considered the gold standard for the diagnosis of severe acute respiratory syndrome coronavirus 2 infection, but false negatives do occur. We built a user-friendly online tool to estimate the probability of having COVID-19 with negative RT-PCR results and thus avoid preventable transmission.

Effectiveness of an Inactivated SARS-CoV-2 Vaccine in Chile.

BACKGROUND: Mass vaccination campaigns to prevent coronavirus disease 2019 (Covid-19) are occurring in many countries; estimates of vaccine effectiveness are urgently needed to support decision making. A countrywide mass vaccination campaign with the use of an inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine (CoronaVac) was conducted in Chile starting on February 2, 2021. METHODS: We used a prospective national cohort, including participants 16 years of age or older who were affiliated with the public national health care system, to assess the effectiveness of the inactivated SARS-CoV-2 vaccine with regard to preventing Covid-19 and related hospitalization, admission to the intensive care unit (ICU), and death. We estimated hazard ratios using the extension of the Cox proportional-hazards model, accounting for time-varying vaccination status. We estimated the change in the hazard ratio associated with partial immunization (≥14 days after receipt of the first dose and before receipt of the second dose) and full immunization (≥14 days after receipt of the second dose). Vaccine effectiveness was estimated with adjustment for individual demographic and clinical characteristics. RESULTS: The study was conducted from February 2 through May 1, 2021, and the cohort included approximately 10.2 million persons. Among persons who were fully immunized, the adjusted vaccine effectiveness was 65.9% (95% confidence interval [CI], 65.2 to 66.6) for the prevention of Covid-19 and 87.5% (95% CI, 86.7 to 88.2) for the prevention of hospitalization, 90.3% (95% CI, 89.1 to 91.4) for the prevention of ICU admission, and 86.3% (95% CI, 84.5 to 87.9) for the prevention of Covid-19-related death. CONCLUSIONS: Our results suggest that the inactivated SARS-CoV-2 vaccine effectively prevented Covid-19, including severe disease and death, a finding that is consistent with results of phase 2 trials of the vaccine. (Funded by Agencia Nacional de Investigación y Desarrollo and others.).

Marginal Bayesian Semiparametric Modeling of Mismeasured Multivariate Interval-Censored Data.

Motivated by data gathered in an oral health study, we propose a Bayesian nonparametric approach for population-averaged modeling of correlated time-to-event data, when the responses can only be determined to lie in an interval obtained from a sequence of examination times and the determination of the occurrence of the event is subject to misclassification. The joint model for the true, unobserved time-to-event data is defined semiparametrically; proportional hazards, proportional odds, and accelerated failure time (proportional quantiles) are all fit and compared. The baseline distribution is modeled as a flexible tailfree prior. The joint model is completed by considering a parametric copula function. A general misclassification model is discussed in detail, considering the possibility that different examiners were involved in the assessment of the occurrence of the events for a given subject across time. We provide empirical evidence that the model can be used to estimate the underlying time-to-event distribution and the misclassification parameters without any external information about the latter parameters. We also illustrate the effect on the statistical inferences of neglecting the presence of misclassification.

A flexible AFT model for misclassified clustered interval-censored data.

Motivated by a longitudinal oral health study, we propose a flexible modeling approach for clustered time-to-event data, when the response of interest can only be determined to lie in an interval obtained from a sequence of examination times (interval-censored data) and on top of that, the determination of the occurrence of the event is subject to misclassification. The clustered time-to-event data are modeled using an accelerated failure time model with random effects and by assuming a penalized Gaussian mixture model for the random effects terms to avoid restrictive distributional assumptions concerning the event times. A general misclassification model is discussed in detail, considering the possibility that different examiners were involved in the assessment of the occurrence of the events for a given subject across time. A Bayesian implementation of the proposed model is described in a detailed manner. We additionally provide empirical evidence showing that the model can be used to estimate the underlying time-to-event distribution and the misclassification parameters without any external information about the latter parameters. We also provide results of a simulation study to evaluate the effect of neglecting the presence of misclassification in the analysis of clustered time-to-event data.

MODELLING COUNTY LEVEL BREAST CANCER SURVIVAL DATA USING A COVARIATE-ADJUSTED FRAILTY PROPORTIONAL HAZARDS MODEL.

Understanding the factors that explain differences in survival times is an important issue for establishing policies to improve national health systems. Motivated by breast cancer data arising from the Surveillance Epidemiology and End Results program, we propose a covariate-adjusted proportional hazards frailty model for the analysis of clustered right-censored data. Rather than incorporating exchangeable frailties in the linear predictor of commonly-used survival models, we allow the frailty distribution to flexibly change with both continuous and categorical cluster-level covariates and model them using a dependent Bayesian nonparametric model. The resulting process is flexible and easy to fit using an existing R package. The application of the model to our motivating example showed that, contrary to intuition, those diagnosed during a period of time in the 1990s in more rural and less affluent Iowan counties survived breast cancer better. Additional analyses showed the opposite trend for earlier time windows. We conjecture that this anomaly has to be due to increased hormone replacement therapy treatments prescribed to more urban and affluent subpopulations.

BM-BC: a Bayesian method of base calling for Solexa sequence data.

Base calling is a critical step in the Solexa next-generation sequencing procedure. It compares the position-specific intensity measurements that reflect the signal strength of four possible bases (A, C, G, T) at each genomic position, and outputs estimates of the true sequences for short reads of DNA or RNA. We present a Bayesian method of base calling, BM-BC, for Solexa-GA sequencing data. The Bayesian method builds on a hierarchical model that accounts for three sources of noise in the data, which are known to affect the accuracy of the base calls: fading, phasing, and cross-talk between channels. We show that the new method improves the precision of base calling compared with currently leading methods. Furthermore, the proposed method provides a probability score that measures the confidence of each base call. This probability score can be used to estimate the false discovery rate of the base calling or to rank the precision of the estimated DNA sequences, which in turn can be useful for downstream analysis such as sequence alignment.

The Polya Tree Sampler: Towards Efficient and Automatic Independent Metropolis-Hastings Proposals.

We present a simple, efficient, and computationally cheap sampling method for exploring an un-normalized multivariate density on ℝ(d), such as a posterior density, called the Polya tree sampler. The algorithm constructs an independent proposal based on an approximation of the target density. The approximation is built from a set of (initial) support points - data that act as parameters for the approximation - and the predictive density of a finite multivariate Polya tree. In an initial "warming-up" phase, the support points are iteratively relocated to regions of higher support under the target distribution to minimize the distance between the target distribution and the Polya tree predictive distribution. In the "sampling" phase, samples from the final approximating mixture of finite Polya trees are used as candidates which are accepted with a standard Metropolis-Hastings acceptance probability. Several illustrations are presented, including comparisons of the proposed approach to Metropolis-within-Gibbs and delayed rejection adaptive Metropolis algorithm.

DPpackage: Bayesian Non- and Semi-parametric Modelling in R.

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian non- and semi-parametric models in R, DPpackage. Currently DPpackage includes models for marginal and conditional density estimation, ROC curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison, and for eliciting the precision parameter of the Dirichlet process prior. To maximize computational efficiency, the actual sampling for each model is carried out using compiled FORTRAN.

A Bayesian Semiparametric Temporally-Stratified Proportional Hazards Model with Spatial Frailties.

Incorporating temporal and spatial variation could potentially enhance information gathered from survival data. This paper proposes a Bayesian semiparametric model for capturing spatio-temporal heterogeneity within the proportional hazards framework. The spatial correlation is introduced in the form of county-level frailties. The temporal effect is introduced by considering the stratification of the proportional hazards model, where the time-dependent hazards are indirectly modeled using a probability model for related probability distributions. With this aim, an autoregressive dependent tailfree process is introduced. The full Kullback-Leibler support of the proposed process is provided. The approach is illustrated using simulated and data from the Surveillance Epidemiology and End Results database of the National Cancer Institute on patients in Iowa diagnosed with breast cancer.