@article{7fe3d3629c9e4d9097d68c99fa64143c,
title = "A Bayesian nonparametric approach to dynamic item-response modeling: An application to the GUSTO cohort study",
abstract = "Statistical analysis of questionnaire data is often performed employing techniques from item-response theory. In this framework, it is possible to differentiate respondent profiles and characterize the questions (items) included in the questionnaire via interpretable parameters. These models are often crosssectional and aim at evaluating the performance of the respondents. The motivating application of this work is the analysis of psychometric questionnaires taken by a group of mothers at different time points and by their children at one later time point. The data are available through the GUSTO cohort study. To this end, we propose a Bayesian semiparametric model and extend the current literature by: (i) introducing temporal dependence among questionnaires taken at different time points; (ii) jointly modeling the responses to questionnaires taken from different, but related, groups of subjects (in our case mothers and children), introducing a further dependency structure and therefore sharing of information; (iii) allowing clustering of subjects based on their latent response profile. The proposed model is able to identify three main groups of mother/child pairs characterized by their response profiles. Furthermore, we report an interesting maternal reporting bias effect strongly affecting the clustering structure of the mother/child dyads.",
keywords = "Dirichlet process, clustering, cohort study, item-response theory, questionnaire data",
author = "Andrea Cremaschi and {De Iorio}, Maria and {Seng Chong}, Yap and Birit Broekman and Meaney, {Michael J} and Kee, {Michelle Z L}",
note = "Funding Information: information Jacob's Foundation, JPB Research Foundation, National Research Foundation Singapore, NMRC/TCR/004-NUS/2008; NMRC/TCR/012-NUHS/2014; Singapore Institute for Clinical SciencesThe GUSTO research is supported by the Singapore National Research Foundation under its Translational and Clinical Research (TCR) Flagship Programme and administered by the Singapore Ministry of Health's National Medical Research Council (NMRC), Singapore - NMRC/TCR/004-NUS/2008; NMRC/TCR/012-NUHS/2014. Additional funding is provided by the Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR). Michael J. Meaney is supported by funding from the JPB Research Foundation and the Jacob's Foundation. Funding Information: The GUSTO research is supported by the Singapore National Research Foundation under its Translational and Clinical Research (TCR) Flagship Programme and administered by the Singapore Ministry of Health's National Medical Research Council (NMRC), Singapore ‐ NMRC/TCR/004‐NUS/2008; NMRC/TCR/012‐NUHS/2014. Additional funding is provided by the Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR). Michael J. Meaney is supported by funding from the JPB Research Foundation and the Jacob's Foundation. Publisher Copyright: {\textcopyright} 2021 Agency for Science, Technology and Research. Statistics in Medicine published by John Wiley & Sons Ltd.",
year = "2021",
month = nov,
day = "30",
doi = "https://doi.org/10.1002/sim.9167",
language = "English",
volume = "40",
pages = "6021--6037",
journal = "Statistics in medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "27",
}