Browsing by Author "Alvares D."

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    A strategy to impute age at onset of a particular condition from external sources
    (SAGE Publications Ltd, 2021) Alvares D.; Paredes F.; Vargas C.; Ferreccio C.; Paredes F.; Ferreccio C.; Vargas C.
    © The Author(s) 2021.A key hypothesis in epidemiological studies is that time to disease exposure provides relevant information to be considered in statistical models. However, the initiation time of a particular condition is usually unknown. Therefore, we developed a multiple imputation methodology for the age at onset of a particular condition, which is supported by incidence data from different sources of information. We introduced and illustrated such a methodology using simulated data in order to examine the performance of our proposal. Then, we analyzed the association of gallstones and fatty liver disease in the Maule Cohort, a Chilean study of chronic diseases, using participants’ risk factors and six sources of information for the imputation of the age-occurrence of gallstones. Simulated studies showed that an increase in the proportion of imputed data does not affect the quality of the estimated coefficients associated with fully observed variables, while the imputed variable slowly reduces its effect. For the Chilean study, the categorized exposure time to gallstones is a significant variable, in which participants who had short and long exposure have, respectively, 26.2% and 29.1% higher chance of getting a fatty liver disease than non-exposed ones. In conclusion, our multiple imputation approach proved to be quite robust both in the linear/logistic regression simulation studies and in the real application, showing the great potential of this methodology.
  • Thumbnail Image
    Item
    A tractable Bayesian joint model for longitudinal and survival data
    (John Wiley and Sons Ltd, 2021) Alvares D.; Rubio F.J.
    © 2021 John Wiley & Sons Ltd.We introduce a numerically tractable formulation of Bayesian joint models for longitudinal and survival data. The longitudinal process is modeled using generalized linear mixed models, while the survival process is modeled using a parametric general hazard structure. The two processes are linked by sharing fixed and random effects, separating the effects that play a role at the time scale from those that affect the hazard scale. This strategy allows for the inclusion of nonlinear and time-dependent effects while avoiding the need for numerical integration, which facilitates the implementation of the proposed joint model. We explore the use of flexible parametric distributions for modeling the baseline hazard function which can capture the basic shapes of interest in practice. We discuss prior elicitation based on the interpretation of the parameters. We present an extensive simulation study, where we analyze the inferential properties of the proposed models, and illustrate the trade-off between flexibility, sample size, and censoring. We also apply our proposal to two real data applications in order to demonstrate the adaptability of our formulation both in univariate time-to-event data and in a competing risks framework. The methodology is implemented in rstan.
  • Thumbnail Image
    Item
    Bayesian survival analysis with BUGS
    (John Wiley and Sons Ltd, 2021) Alvares D.; Lázaro E.; Gómez-Rubio V.; Armero C.
    © 2021 John Wiley & Sons Ltd.Survival analysis is one of the most important fields of statistics in medicine and biological sciences. In addition, the computational advances in the last decades have favored the use of Bayesian methods in this context, providing a flexible and powerful alternative to the traditional frequentist approach. The objective of this article is to summarize some of the most popular Bayesian survival models, such as accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data. Moreover, an implementation of each presented model is provided using a BUGS syntax that can be run with JAGS from the R programming language. Reference to other Bayesian R-packages is also discussed.
  • Thumbnail Image
    Item
    Teaching through dance: An opportunity to introduce physically active academic lessons
    (Elsevier Ltd, 2021) Beserra V.; Nussbaum M.; Navarrete M.; Alvares D.
    © 2021 Elsevier LtdIntroducing physically active academic lessons into the school routine implies a significant change in educational practice. The aim of this study is to explore the acceptability and feasibility of delivering physically active academic lessons through dance. During five training sessions, 37 teachers created a series of dance routines based on mathematical functions. At the end, all teachers participated in focus groups. The results of the thematic analysis revealed high levels of interest among the teachers in incorporating physically active academic lessons into the school routine. Teachers also identified the main barriers to implementation and constructed lesson plans.