Browsing by Author "Gutierrez, Ivan"

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    A new flexible Bayesian hypothesis test for multivariate data
    (2023) Gutierrez, Ivan; Gutierrez, Luis; Alvares, Danilo
    We propose a Bayesian hypothesis testing procedure for comparing the multivariate distributions of several treatment groups against a control group. This test is derived from a flexible model for the group distributions based on a random binary vector such that, if its jth element equals one, then the jth treatment group is merged with the control group. The group distributions' flexibility comes from a dependent Dirichlet process, while the latent vector prior distribution ensures a multiplicity correction to the testing procedure. We explore the posterior consistency of the Bayes factor and provide a Monte Carlo simulation study comparing the performance of our procedure with state-of-the-art alternatives. Our results show that the presented method performs better than competing approaches. Finally, we apply our proposal to two classical experiments. The first one studies the effects of tuberculosis vaccines on multiple health outcomes for rabbits, and the second one analyzes the effects of two drugs on weight gain for rats. In both applications, we find relevant differences between the control group and at least one treatment group.
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    Factors associated to the duration of COVID-19 lockdowns in Chile
    (2022) Pavani, Jessica; Cerda, Jaime; Gutierrez, Luis; Varas, Ines; Gutierrez, Ivan; Jofre, Leonardo; Ortiz, Oscar; Arriagada, Gabriel
    During the first year of the COVID-19 pandemic, several countries have implemented non-pharmacologic measures, mainly lockdowns and social distancing, to reduce the spread of the SARS-CoV-2 virus. These strategies varied widely across nations, and their efficacy is currently being studied. This study explores demographic, socioeconomic, and epidemiological factors associated with the duration of lockdowns applied in Chile between March 25th and December 25th, 2020. Joint models for longitudinal and time-to-event data were used. In this case, the number of days under lockdown for each Chilean commune and longitudinal information were modeled jointly. Our results indicate that overcrowding, number of active cases, and positivity index are significantly associated with the duration of lockdowns, being identified as risk factors for longer lockdown duration. In short, joint models for longitudinal and time-to-event data permit the identification of factors associated with the duration of lockdowns in Chile. Indeed, our findings suggest that demographic, socioeconomic, and epidemiological factors should be used to define both entering and exiting lockdown.
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    Implementing blopmatching in Stata
    (2021) Diaz, Juan D.; Gutierrez, Ivan; Rivera, Jorge
    The blopmatching estimator for average treatment effects in observational studies is a nonparametric matching estimator proposed by Diaz, Rau, and Rivera (2015, Review of Economics and Statistics 97: 803-812). This approach uses the solutions of linear programming problems to build the weighting schemes that are used to impute the missing potential outcomes. In this article, we describe blopmatch, a new command that implements these estimators.