Browsing by Author "Jara Vallejos, Alejandro Antonio"

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    Dependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials
    (Institute of Mathematical Statistics, 2022) Wehrhahn, Claudia; Barrientos, Andrés F.; Jara Vallejos, Alejandro Antonio
    We discuss Bayesian nonparametric procedures for the regression analysis of compositional responses, that is, data supported on a multivariate simplex. The procedures are based on a modified class of multivariate Bernstein polynomials and on the use of dependent stick-breaking processes. A general model and two simplified versions of the general model are discussed. Appealing theoretical properties such as continuity, association structure, support, and consistency of the posterior distribution are established. Additionally, we exploit the use of spike-and-slab priors for choosing the version of the model that best adapts to the complexity of the underlying true data-generating distribution. The performance of the proposed model is illustrated in a simulation study and in an application to solid waste data from Colombia.
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    Effectiveness of an Inactivated SARS-CoV-2 Vaccine. Reply
    (2021) Jara Vallejos, Alejandro Antonio; Undurraga Fourcade, Eduardo Andrés; Araos, Rafael
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    Effectiveness of the second COVID-19 booster against Omicron: a large-scale cohort study in Chile
    (2023) Jara Vallejos, Alejandro Antonio; Cuadrado, Cristóbal; Undurraga Fourcade, Eduardo Andrés; García, Christian; Nájera, Manuel; Bertoglia, María Paz; Vergara, Verónica; Fernández, Jorge; García-Escorza, Heriberto; Araos, Rafael
    In light of the ongoing COVID-19 pandemic and the emergence of new SARSCoV-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 nonvaccination, 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 ofimmunity. 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.
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    High Burden of Intestinal Colonization With Antimicrobial-Resistant Bacteria in Chile: An Antibiotic Resistance in Communities and Hospitals (ARCH) Study
    (2023) Araos, Rafael; Smith, Rachel M.; Styczynski, Ashley; Sánchez Barría, Felipe Andrés; Acevedo, Johanna; Maureira, Lea; Paredes, Catalina; Gonzalez, Maite; Rivas, Lina; Spencer-Sandino, Maria; Peters, Anne; Khan, Ayesha; Sepulveda, Dino; Rojas Wettig, Loreto; Rioseco, Maria Luisa; Usedo, Pedro; Rojas Soto, Pamela; Huidobro, Laura Andrea; Ferreccio Readi, Catterina; Park, Benjamin J.; Undurraga Fourcade, Eduardo Andrés; D'Agata, Erika M. C.; Jara Vallejos, Alejandro Antonio; Munita, Jose M.
    We report a high colonization burden resulting from antimicrobial-resistant Gram-negative bacteria in hospitals and a community in Chile. Strikingly, 29% (95% confidence interval, 24-34) of community-dwelling adults carried extended-spectrum cephalosporin-resistant Enterobacterales, highlighting the magnitude of the community reservoir of antimicrobial resistance., 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.
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    Identifying outbreaks in sewer networks: An adaptive sampling scheme under network's uncertainty
    (2024) Baboun Larach, José; Beaudry, Isabelle S.; Castro Cepero, Luis Mauricio; Gutiérrez González, Felipe Iván; Jara Vallejos, Alejandro Antonio; Rubio Orellana, Benjamín Eduardo; Verschae, José
    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.
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    On the small sample behavior of Dirichlet process mixture models for data supported on compact intervals
    (2019) Wehrhahn, Claudia; Jara Vallejos, Alejandro Antonio; Barrientos, Andrés F.
    Bayesian nonparametric models provide a general framework for flexible statistical modeling of modern complex data sets. We compare a rate-optimal and rate-suboptimal Bayesian nonparametric model for density estimation for data supported on a compact interval, by means of the analyses of simulated and real data. The results show that rate-optimal models are not uniformly better, across sample sizes, with respect to the way in which the posterior mass concentrates around a true model and that suboptimal models can outperform the optimal ones, even for relatively large sample sizes.
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    Quantitative Susceptibility Mapping MRI in Deep-Brain Nuclei in First-Episode Psychosis
    (2023) García Saborit, Marisleydis; Jara Vallejos, Alejandro Antonio; Muñoz Camelo, Néstor Andrés; Milovic, Carlos; Tepper, Angeles; Alliende Correa, Luz María; Mena, Carlos; Iruretagoyena Bruce, Bárbara Arantzazu; Ramírez Mahaluf, Juan Pablo; Diaz, Camila; Nachar, Rubén; Castaneda, Carmen Paz; Gonzalez, Alfonso; Undurraga, Juan; Crossley, Nicolás; Tejos Núñez, Cristián Andrés
    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.
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    Surviving fully Bayesian nonparametric regression models
    (Oxford University Press, 2013) Hanson, Timothy E.; Jara Vallejos, Alejandro Antonio
    This chapter compares two Bayesian nonparametric models that generalize the accelerated failure time model, based on recent work on probability models for predictor-dependent probability distributions. It begins by reviewing commonly used semiparametric survival models. It then discusses the Bayesian nonparametric priors used in the generalizations of the accelerated failure time (AFT) model. Next, the two generalizations of the accelerated failure time model are introduced and compared by means of real-life data analyses. The models correspond to generalizations of AFT models based on dependent extensions of the Dirichlet process (DP) and Polya tree (PT) priors. Advantages of the induced survival regression models include ease of interpretability and computational tractability.
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    The Dependent Dirichlet Process and Related Models
    (INST MATHEMATICAL STATISTICS-IMS, 2022) Quintana Quintana, Fernando; Muller, Peter; Jara Vallejos, Alejandro Antonio; MacEachern, Steven N.
    Standard regression approaches assume that some finite number of the response distribution characteristics, such as location and scale, change as a (parametric or nonparametric) function of predictors. However, it is not always appropriate to assume a location/scale representation, where the error distribution has unchanging shape over the predictor space. In fact, it often happens in applied research that the distribution of responses under study changes with predictors in ways that cannot be reasonably represented by a finite dimensional functional form. This can seriously affect the answers to the scientific questions of interest, and therefore more general approaches are indeed needed. This gives rise to the study of fully nonparametric regression models. We review some of the main Bayesian approaches that have been employed to define probability models where the complete response distribution may vary flexibly with predictors. We focus on developments based on modifications of the Dirichlet process, historically termed dependent Dirichlet processes, and some of the extensions that have been proposed to tackle this general problem using nonparametric approaches.
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    The role of body mass index at diagnosis of colorectal cancer on Black-White disparities in survival: a density regression mediation approach
    (2022) Devick, Katrina L.; Valeri, Linda; Chen, Jarvis; Jara Vallejos, Alejandro Antonio; Bind, Marie-Abele; Coull, Brent A.
    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.