Research
Research
Under Review
Silva-Neto, D.; Rustand, D.; Rue, H.; Alvares, D.; Tomazella, V.: A new approach for Bayesian joint modeling of longitudinal and cure-survival outcomes using the defective Gompertz distribution. [arXiv]
Molina, K.C.; Martínez-Minaya, J.; Alvares, D.; Tomazella, V.: A Bayesian survival model induced by hurdle zero-modified power series discrete frailty with dispersion: an application in lung cancer. [arXiv]
Galán-Arcicollar, C.; Alvares, D.; Najera-Zuloaga, J.; Lee, D.J.: Bayesian joint modeling for longitudinal PRO and survival data: A 5-year study of COPD patients analysis.
2025
[58] Alvares, D.; Barrett, J.K.; Mercier, F.; Schulze, J.; Yiu, S.; Castro, F.; Roumpanis, S.; Zhu, Y.: A Bayesian joint model of multiple longitudinal and categorical outcomes with application to multiple myeloma using permutation-based variable importance. To appear in Annals of Applied Statistics, 2025. [arXiv]
[57] Gutiérrez, L.; Gutiérrez, I.; Alvares, D.: Bayesian flexible models for ANOVA-type data. Bayesian Analysis, 2025. [DOI]
[56] García, R.; Meneses, A.; Veas, M.G.; Alvares, D.; Arriagada, S.; Nussbaum, M.: The role of core practices, critical thinking, and communication skills in the development of teacher adaptive expertise. To appear in Technology, Pedagogy and Education, 2025.
[55] Alvares, D.; Meza, C.; De la Cruz, R.: Bayesian inference for nonlinear mixed-effects location scale and interval-censoring cure-survival models: An application to pregnancy miscarriage. Statistical Methods in Medical Research, 34(8): 1525-1533, 2025. [DOI]
[54] Spencer-Sandino, M.; Godoy, F.; Huidobro, L.; Alvares, D.; Cruz, F.; Marco, C.; Garrido, M.; Cabrera, D.; Arab, J.P.; Arrese, M.; Barrera, F.; Ferreccio, C.: New steatotic liver disease criteria diagnostic performance in an agricultural population in Chile. Annals of Hepatology, 30(2): 1-6, 2025. [DOI]
[53] Chen, S; Alvares, D.; Palma, M.; Barrett, J.K.: Bayesian shared parameter joint models for heterogeneous populations. Statistics and Computing, 35(5): 1-17, 2025. [DOI]
[52] Pérez-Jeldres, T.; Bustamante, M.L.; Alvares, D.; Álvarez-Lobos, M.; Kalmer, L.; Azocar, L.; Segovia-Melero, R.; Ascui, G.; Aguilar, N.; Estela, R.; Hernández-Rocha, C.; Candia, R.; González, M.; Silva, V.; De la Vega, A.; Arriagada, E.; Serrano, C.A.; Pávez-Ovalle, C.; Moraga-Quinteros, C.; Miquel, J.F.; Di Genova, A.: Impact of Amerindian ancestry on clinical outcomes in Crohn's disease and ulcerative colitis in a Latino population. Scientific Reports, 15(1): 1-15, 2025. [DOI]
[51] Alvares, D.; Barrett, J.K.; Mercier, F.; Roumpanis, S.; Yiu, S.; Castro, F.; Schulze, J.; Zhu, Y.: A Bayesian joint model of multiple nonlinear longitudinal and competing risks outcomes for dynamic prediction in multiple myeloma: joint estimation and corrected two-stage approaches. Statistics in Medicine, 44(3-4): 1-13, 2025. [DOI]
2024
[50] Hochschild-Ovalle, H.; Nussbaum, M.; Claro, S.; Espinosa, P.; Alvares, D.: Happiness at school and its relationship with academic achievement. Education Sciences, 14(12): 1-18, 2024. [DOI]
[49] Chen, S.; Alvares, D.; Jackson, C.; Marshall, T.; Nirantharakumar, K.; Richardson, S.; Saunders, C.L.; Barrett, J.K.: Bayesian blockwise inference for joint models of longitudinal and multistate data with application to longitudinal multimorbidity analysis. Statistical Methods in Medical Research, 33(11-12): 2027-2042, 2024. [DOI]
[48] Gutiérrez, I.; Alvares, D.; Gutiérrez, L.: A Bayesian flexible model for testing Granger causality. Econometrics and Statistics, 2024. [DOI]
[47] Gallardo-Estrada, C.; Nussbaum, M.; Pinto, M.; Alvares, D.; Alario-Hoyos, C.: Enhancing grit and critical thinking in rural primary students: impact of a targeted educational intervention. Education Sciences, 14(9): 1-15, 2024. [DOI]
[46] Galán-Arcicollar, C.; Alvares, D.; Najera-Zuloaga, J.; Lee, D.J.: A joint modelling approach for longitudinal patient-reported outcomes and survival analysis. Proceedings of the 38th International Workshop on Statistical Modelling, 124-128, 2024. [DOI]
[45] Alvares, D.; van Niekerk, J.; Krainski, E.T.; Rue, H.; Rustand, D.: Bayesian survival analysis with INLA. Statistics in Medicine, 43(20): 3975-4010, 2024. [DOI]
[44] Vicuña, L.; Barrientos, E.; Leiva-Yamaguchi, V.; Alvares, D.; Mericq, V.; Pereira, A.; Eyheramendy, S.: Joint models reveal genetic architecture of pubertal stage transitions and their association with BMI in admixed Chilean population. Human Molecular Genetics, 33(19): 1660-1670, 2024. [DOI]
[43] Alvares, D.; Mercier, F.: Bridging the gap between two-stage and joint models: the case of tumor growth inhibition and overall survival models. Statistics in Medicine, 43(17): 3280-3293, 2024. [DOI]
[42] Beserra, V.; Nussbaum, M.; Navarrete, M.; Garrido, N.; Alvares, D.: The use of physically active academic lessons during the transition to face-to-face classes. SAGE Open, 14(2): 1-20, 2024. [DOI]
2023
[41] Pérez-Jeldres, T.; Magne, F.; Ascui, G.; Alvares, D.; Orellana, M.; Álvarez-Lobos, M.; Hernández-Rocha, C.; Azocar, L.; Aguilar, N.; Espino, A.; Estela, R.; Escobar, S.; Zazueta, A.; Baéz, P.; Silva, V.; De la Vega, A.; Arriagada, E.; Pávez-Ovalle, C.; Diaz-Asencio, A.; Travisany, D.; Miquel, J.F.; Villablanca, E.J.; Kronenberg, M.; Bustamante, M.L.: Amerindian ancestry proportion as a risk factor for inflammatory bowel diseases: results from a Latin American Andean cohort. Frontiers in Medicine, 10: 1-12, 2023. [DOI]
[40] Alvares, D.; Leiva-Yamaguchi, V.: A two-stage approach for Bayesian joint models: reducing complexity while maintaining accuracy. Statistics and Computing, 33(5): 1-11, 2023. [DOI]
[39] López, F.; Contreras, M.; Nussbaum, M.; Paredes, R.; Gelerstein, D.; Alvares, D.; Chiuminatto, P.: Developing critical thinking in technical and vocational education and training. Education Sciences, 13(6): 1-21, 2023. [DOI]
[38] Gutiérrez, I.; Gutiérrez, L.; Alvares, D.: A new flexible Bayesian hypothesis test for multivariate data. Statistics and Computing, 33(2): 1-16, 2023. [DOI]
[37] Vicuña, L.; Barrientos, E.; Norambuena, T.; Alvares, D.; Gana, J.C.; Leiva-Yamaguchi, V.; Meza, C.; Santos, J.L.; Mericq, V.; Pereira, A.; Eyheramendy, S.: New insights from GWAS on BMI-related growth phenotypes in a longitudinal cohort of admixed children with Native American and European ancestry. iScience, 26(2): 1-16, 2023. [DOI]
2022
[36] Gutiérrez, I.; Gutiérrez, L.; Alvares, D.: A Bayesian nonparametric test for cross-group differences relative to a control. New Frontiers in Bayesian Statistics: BAYSM 2021, Springer Proceedings in Mathematics & Statistics, 405: 79-89, 2022. [DOI]
[35] Pérez-Jeldres, T.; Pizarro, B.; Ascui, G.; Orellana, M.; Cerda-Villablanca, M.; Alvares, D.; De la Vega, A.; Canistra, M.; Cornejo, B.; Baéz, P.; Silva, V.; Arriagada, E.; Rivera-Nieves, J.; Estela, R.; Hernández-Rocha, C.; Álvarez-Lobos, M.; Tobar, F.: Ethnicity influences phenotype and clinical outcomes: comparing a South American with a North American inflammatory bowel disease cohort. Medicine, 101(36): 1-12, 2022. [DOI]
[34] Cortázar, C.; Nussbaum, M.; Alario-Hoyos, C.; Goñi, J.; Alvares, D.: The impacts of scaffolding socially shared regulation on teamwork in an online project-based course. The Internet and Higher Education, 55: 1-22, 2022. [DOI]
[33] Reveco-Quiroz, P.; Sandoval-Díaz; J.; Alvares, D.: Bayesian modeling for pro-environmental behavior data: sorting and selecting relevant variables. Stochastic Environmental Research and Risk Assessment, 36: 3961-3977, 2022. [DOI]
[32] Rubio, F.J.; Alvares, D.; Redondo-Sánchez, D.; Marcos-Gragera, R.; Sánchez, M.J.; Luque-Fernández, M.A.: Bayesian variable selection and survival modeling: assessing the most important comorbidities that impact lung and colorectal cancer survival in Spain. BMC Medical Research Methodology, 22(95): 1-14, 2022. [DOI]
[31] Rojas, M.; Nussbaum, M.; Guerrero, O.; Chiuminatto, P.; Greiff, S.; Del Rio, R.; Alvares, D.: Integrating collaborative script and group awareness to support group regulation and emotions towards collaborative problem solving. International Journal of Computer-Supported Collaborative Learning, 17: 135-168, 2022. [DOI]
[30] Rodríguez, M.F.; Nussbaum, M.; Yunis, L.; Reyes, T.; Alvares, D.; Joublan, J.; Navarrete, P.: Using scaffolded feedforward and peer feedback to improve problem-based learning in large classes. Computers & Education, 182: 1-13, 2022. [DOI]
2021
[29] Beserra, V.; Nussbaum, M.; Navarrete, M.; Alvares, D.: Teaching through dance: an opportunity to introduce physically active academic lessons. Teaching and Teacher Education, 106: 1-13, 2021. [DOI]
[28] Alvares, D.; Rubio, F.J.: A tractable Bayesian joint model for longitudinal and survival data. Statistics in Medicine, 40(19): 4213-4229, 2021. [DOI]
[27] Alvares, D.; Paredes, F.; Vargas, C.; Ferreccio, C.: A strategy to impute age at onset of a particular condition from external sources. Statistical Methods in Medical Research, 30(8): 1771-1781, 2021. [DOI]
[26] Cortázar, C.; Nussbaum, M.; Harcha, J.; Alvares, D.; López, F.; Goñi, J.; Cabezas, V.: Promoting critical thinking in an online, project-based course. Computers in Human Behavior, 119: 1-18, 2021. [DOI]
[25] Alvares, D.; Lázaro, E.; Gómez‐Rubio, V.; Armero, C.: Bayesian survival analysis with BUGS. Statistics in Medicine, 40(12): 2975-3020, 2021. [DOI]
[24] Reis, H.M.; Alvares, D.; Jaques, P.A.; Isotani, S.: A proposal of model of emotional regulation in intelligent learning environments. Informatics in Education, 20(2): 317-332, 2021. [DOI]
[23] Alvares, D.; Armero, C.; Forte, A.; Chopin, N.: Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data. Statistical Modelling, 21(1-2): 161-181, 2021. [DOI]
[22] Reis, H.M.; Alvares, D.; Jaques, P.A.; Isotani, S.: Customization of emotional regulation according to the personality of students in intelligent tutoring systems. Brazilian Journal of Computers in Education, 29: 48-72, 2021. [DOI]
[21] Araneda, D.; Galarce, J.; Alvares, D.; Nussbaum, M.: What to learn? Socialization of the subject hierarchy in schools. Educational Studies, 57(1): 58-77, 2021. [DOI]
[20] Lázaro, E.; Armero, C.; Alvares, D.: Bayesian regularization for flexible baseline hazard functions in Cox survival models. Biometrical Journal, 63(1): 7-26, 2021. [DOI]
[19] Leiva-Yamaguchi, V.; Alvares, D.: A two-stage approach for Bayesian joint models of longitudinal and survival data: correcting bias with informative prior. Entropy, 23(1): 1-10, 2021. [DOI]
2020
[18] Goñi, J.; Cortázar, C.; Alvares, D.; Donoso, U.; Miranda, C.: Is teamwork different online versus face-to-face? A case in Engineering education. Sustainability, 12(24): 1-18, 2020. [DOI]
[17] Chamorro, C.; Alvares, D.; Berger, S.; Balocci, F.; Rodriguez, X.; Soza, F.: Psychometric properties of the Chilean version of the Quick Disabilities of the Arm, Shoulder and Hand (Quick DASH) questionnaire for patients with shoulder disorders. Archivos de Medicina del Deporte, 37(5): 305-309, 2020. [DOI]
[16] Oliveira, C.M.; Tureck, L.V.; Alvares, D.; Liu, C.; Horimoto, A.R.V.R.; Balcells, M.; Alvim, R.O.; Krieger, J.E.; Pereira, A.C.: Relationship between marital status and incidence of type 2 diabetes mellitus in a Brazilian rural population: the Baependi Heart Study. PLOS ONE, 15(8): 1-10, 2020. [DOI]
[15] Ovalle, C.; Alvares, D.: A Bayesian graphical and probabilistic proposal for bias analysis. Quantitative Psychology: IMPS 2019, Springer International Publishing, 322: 69-78, 2020. [DOI]
[14] Vieira, C.L.Z.; Garshick, E.; Alvares, D.; Schwartz, J.; Huang, S.; Vokonas, P.; Gold, D.R.; Koutrakis, P.: Association between ambient beta particle radioactivity and lower hemoglobin concentrations in a cohort of elderly men. Environment International, 139: 1-7, 2020. [DOI]
[13] Pavani, J.; Alvares, D.: Statistically validating patient self-reporting questionnaires in medicine. SAGE Research Methods Cases: Medicine and Health, 1-19, 2020. [DOI]
[12] Oliveira, C.M.; Tureck, L.V.; Alvares, D.; Liu, C.; Horimoto, A.R.V.R.; Alvim, R.O.; Krieger, J.E.; Pereira, A.C.: Cardiometabolic risk factors correlated with the incidence of dysglycaemia in a Brazilian normoglycaemic sample: the Baependi Heart Study cohort. Diabetology & Metabolic Syndrome, 12(6): 1-7, 2020. [DOI]
2019
[11] Alvares, D.; Haneuse, S.; Lee, C.; Lee, K.H.: SemiCompRisks: an R package for the analysis of independent and cluster-correlated semi-competing risks data. The R Journal, 11(1): 376-400, 2019. [DOI]
[10] Vieira, C.L.Z.; Alvares, D.; Blomberg, A.; Schwartz, J.; Coull, B.; Huang, S.; Koutrakis, P.: Geomagnetic disturbances driven by solar activity enhance total and cardiovascular mortality risk in 263 U.S. cities. Environmental Health, 18(83): 1-10, 2019. [DOI]
2018
[9] Reis, H.M.; Alvares, D.; Jaques, P.A.; Isotani, S.: Analysis of permanence time in emotional states: a case study using educational software. Intelligent Tutoring Systems, Springer International Publishing, 180-190, 2018. [DOI]
[8] Cacheiro, P.; Lorenzo-Arribas, A.; Lázaro, E.; Alvares, D.; Barrio, I.; Bofill Roig, M.; Branco, M.; Gómez-Mateu, M.; Vilor-Tejedor, N.; Pérez-Haro, M.J.: Reproducibility, visibility and diversity: current trends in Biostatistics viewed by young researchers. Boletín de Estadística e Investigación Operativa, 40(1): 73-91, 2018. [ISSN]
[7] Alvares, D.; Armero, C.; Forte, A.: What does objective mean in a Dirichlet-multinomial framework? International Statistical Review, 86(1): 106-118, 2018. [DOI]
[6] Isotani, S.; Reis, H.M.; Alvares, D.; Brandão, A.A.F.; Brandão, L.O.: A DGS gesture dictionary for modelling on mobile devices. Interactive Learning Environments, 26(3): 320-336, 2018. [DOI]
2017
[5] Rué, M.; Andrinopoulou, E.R.; Alvares, D.; Armero, C.; Forte, A.; Blanch, L.: Bayesian joint modeling of bivariate longitudinal and competing risks data: an application to study patient-ventilator asynchronies in critical care patients. Biometrical Journal, 59(6): 1184-1203, 2017. [DOI]
[4] Alvares, D.; Armero, C.; Forte, A.; Serra, J.; Galipienso, L.; Rubio, L.: Incidence and control of black spot syndrome of tiger nut. Annals of Applied Biology, 171(3): 417-423, 2017. [DOI]
[3] Sanz-Puig, M.; Lázaro, E.; Armero, C.; Alvares, D.; Martínez, A.; Rodrigo, D.: S. Typhimurium virulence changes caused by exposure to different non-thermal preservation treatments using C. elegans as a model organism. International Journal of Food Microbiology, 262: 49-54, 2017. [DOI]
[2] Alvares, D.; Armero, C.; Forte, A.; Chopin, N.: Sequential Monte Carlo methods in random intercept models for longitudinal data. Bayesian Statistics in Action: BAYSM 2016, Springer Proceedings in Mathematics & Statistics, 194: 3-9, 2017. [DOI]
2016
[1] Alvares, D.; Armero, C.; Forte, A.; Rubio, L.: Exploring Bayesian models to evaluate control procedures for plant disease. Statistics and Operations Research Transactions, 40(1): 139-152, 2016. [DOI]