DSpace Angular :: Browsing by Author "Jara, Alejandro"

Browsing by Author "Jara, Alejandro"

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A Bayesian semiparametric partially PH model for clustered time-to-event data
(2018) Nipoti, Bernardo; Jara, Alejandro; Guindani, Michele
A Bayesian Semiparametric Temporally-Stratified Proportional Hazards Model with Spatial Frailties
(INT SOC BAYESIAN ANALYSIS, 2012) Hanson, Timothy E.; Jara, Alejandro; Zhao, Luping
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 data and data from the Surveillance Epidemiology and End Results database of the National Cancer Institute on patients in Iowa diagnosed with breast cancer.
A time series model for responses on the unit interval
(2013) Jara, Alejandro; Nieto Barajas, L.; Quintana Quintana, Fernando
Bayesian Nonparametric Approaches for ROC Curve Inference
(2015) Calhau Fernandes, Inacio De Carvalho Vanda; Jara, Alejandro; Bras De Carvalho, Miguel
The development of medical diagnostic tests is of great importance in clinical practice, public health, and medical research. The receiver operating characteristic (ROC) curve is a popular tool for evaluating the accuracy of such tests. We review Bayesian nonparametric methods based on Dirichlet process mixtures and the Bayesian bootstrap for ROC curve estimation and regression. The methods are illustrated by means of data concerning diagnosis of lung cancer in women.
Bayesian Nonparametric Data Analysis
(2015) Müller, Peter; Quintana Quintana, Fernando Andrés; Jara, Alejandro; Hanson, Tim
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones.The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages
Bayesian nonparametric ROC regression modeling
(2013) Calhau Fernandes Inacio de Carvalho, Vanda; Jara, Alejandro; Hanson, Timothy E.; Bras De Carvalho, Miguel
Clustering and feature allocation
(2015) Müller, Peter; Quintana Quintana, Fernando; Jara, Alejandro; Hanson, Tim
Correcting for misclassification for a monotone disease process with an application in dental research
(2010) García Zattera, María José; Mutsvari, T.; Jara, Alejandro; Declerck, D.; Lesaffre, E.
DPpackage: Bayesian Semi- and Nonparametric Modeling in R
(JOURNAL STATISTICAL SOFTWARE, 2011) Jara, Alejandro; Hanson, Timothy E.; Quintana, Fernando A.; Mueller, Peter; Rosner, Gary L.
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 nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic 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, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.
Effectiveness and duration of a second COVID-19 vaccine booster
(2022) Jara, Alejandro; Cuadrado, Cristobal; Undurraga Fourcade, Eduardo Andrés; García, Christian; Najera, Manuel; Bertoglia, María Paz; Vergara, Verónica; Fernández, Jorge; García, Heriberto; Araos, Rafael
Using a prospective national cohort of 3.75 million individuals aged 20 or older, we evaluated the effectiveness against COVID-19 related ICU admissions and death of mRNA-based second vaccine boosters for four different three-dose background regimes: BNT162b2 primary series plus a homologous booster, and CoronaVac primary series plus an mRNA booster, a homologous booster, and a ChAdOx-1 booster. We estimated the vaccine effectiveness weekly from February 14 to August 15, 2022, by estimating hazard ratios of immunization over non-vaccination, accounting for relevant confounders. The overall adjusted effectiveness of a second mRNA booster shot was 88.2% (95%CI, 86.2-89.9) and 90.5% (95%CI 89.4-91.4) against ICU admissions and death, respectively. Vaccine effectiveness showed a mild decrease for all regimens and outcomes, probably associated with the introduction of BA.4 and BA.5 Omicron sub-lineages and immunity waning. The duration of effectiveness suggests that no additional boosters are needed six months following a second booster shot.
Effectiveness of an Inactivated SARS-CoV-2 Vaccine in Chile
(MASSACHUSETTS MEDICAL SOC, 2021) Jara, Alejandro; Undurraga, Eduardo A.; Gonzalez, Cecilia; Paredes, Fabio; Fontecilla, Tomas; Jara, Gonzalo; Pizarro, Alejandra; Acevedo, Johanna; Leo, Katherine; Leon, Francisco; Sans, Carlos; Leighton, Paulina; Suarez, Pamela; Garcia Escorza, Heriberto; Araos, Rafael
Inactivated SARS-CoV-2 Vaccine in Chile In a national prospective cohort study involving 10.2 million participants in Chile, the effectiveness of an inactivated SARS-CoV-2 vaccine, which had been developed in China and administered in two doses 28 days apart, was estimated. Effectiveness among fully immunized persons was estimated at 65.9% for Covid-19 and at 87.5% for hospitalization, 90.3% for ICU admission, and 86.3% for death.
Effectiveness of an Inactivated SARS-CoV-2 Vaccine REPLY
(MASSACHUSETTS MEDICAL SOC, 2021) Jara, Alejandro; Undurraga, Eduardo A.; Araos, Rafael
Effectiveness of CoronaVac in children 3-5 years of age during the SARS-CoV-2 Omicron outbreak in Chile
(NATURE PORTFOLIO, 2022) Jara, Alejandro; Undurraga, Eduardo A.; Zubizarreta, Jose R.; Gonzalez, Cecilia; Acevedo, Johanna; Pizarro, Alejandra; Vergara, Veronica; Soto-Marchant, Mario; Gilabert, Rosario; Flores, Juan Carlos; Suarez, Pamela; Leighton, Paulina; Eguiguren, Pablo; Carlos Rios, Juan; Fernandez, Jorge; Garcia-Escorza, Heriberto; Araos, Rafael
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.
Fully Nonparametric Regression Modelling of Misclassified Censored Time-to-Event Data
(2015) Jara, Alejandro; Garcia Zattera, María José; Komárek, Arnost
We propose a fully nonparametric modelling approach for time-to-event regression data, when the response of interest 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 covariate-dependent time-to-event distributions are modelled using a linear dependent Dirichlet process mixture model. A general misclassification model is discussed, considering the possibility that different examiners were involved in the assessment of the occurrence of the events for a given subject across time. An advantage of the proposed model is that the underlying time-to-event distributions and the misclassification parameters can be estimated without any external information about the latter parameters.
Gastric cancer is related to early Helicobacter pylori infection in a high-prevalence country
(AMER ASSOC CANCER RESEARCH, 2007) Ferreccio, Catterina; Rollan, Antonio; Harris, Paul R.; Serrano, Carolina; Gederlini, Alessandra; Margozzini, Paula; Gonzalez, Claudia; Aguilera, Ximena; Venegas, Alejandro; Jara, Alejandro
Background and Aims: Chile ranks fifth in the world among countries with the highest incidence of gastric cancer. The aim was to quantify the association between Helicobacter pylori infection and gastric cancer mortality at the county of residence.
Modeling county level breast cancer survival data using a covariate-adjusted frailty proportional hazards model
(2015) Zhou, Haiming; Hanson, Timothy; Jara, Alejandro; Zhang, Jiajia
Modeling of Multivariate Monotone Disease Processes in the Presence of Misclassification
(AMER STATISTICAL ASSOC, 2012) García Zattera, María José; Jara, Alejandro; Lesaffre, Emmanuel; Marshall Rivera, Guillermo
Neutralizing antibodies induced by homologous and heterologous boosters in CoronaVac vaccines in Chile
(2023) Acevedo, Johanna; Acevedo, Monica L.; Gaete-Argel, Aracelly; Araos, Rafael; Gonzalez, Cecilia; Espinoza, Daniela; Rivas, Solange; Pizarro, Pablo; Jarpa, Stephan; Soto-Rifo, Ricardo; Jara, Alejandro; Valiente-Echeverria, Fernando
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.
On dependent Dirichlet processes for general Polish spaces
(2024) Iturriaga, Andres; Long, Carlos A. Sing; Jara, Alejandro
We study Dirichlet process-based models for sets of predictor- dependent probability distributions, where the domain and predictor space are general Polish spaces. We generalize the definition of dependent Dirichlet processes, originally constructed on Euclidean spaces, to more general Polish spaces. We provide sufficient conditions under which dependent Dirichlet processes and dependent Dirichlet process mixture models have appealing properties regarding continuity (weak and strong), association structure, and support (under different topologies). The results can be easily extended to more general dependent stick -breaking processes.
On the Bayesian Nonparametric Generalization of IRT-Type Models
(2011) San Martín Gutiérrez, Ernesto Javier; Jara, Alejandro; Rolin, Jean-Marie; Mouchart, Michel
We study the identification and consistency of Bayesian semiparametric IRT-type models, where the uncertainty on the abilities' distribution is modeled using a prior distribution on the space of probability measures. We show that for the semiparametric Rasch Poisson counts model, simple restrictions ensure the identification of a general distribution generating the abilities, even for a finite number of probes. For the semiparametric Rasch model, only a finite number of properties of the general abilities' distribution can be identified by a finite number of items, which are completely characterized. The full identification of the semiparametric Rasch model can be only achieved when an infinite number of items is available. The results are illustrated using simulated data.

Browsing by Author "Jara, Alejandro"

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