{"id":22642,"date":"2022-11-17T08:13:09","date_gmt":"2022-11-17T13:13:09","guid":{"rendered":"https:\/\/carleton.ca\/math\/?p=22642"},"modified":"2022-11-17T08:13:27","modified_gmt":"2022-11-17T13:13:27","slug":"probability-statistics-seminar-3","status":"publish","type":"post","link":"https:\/\/carleton.ca\/math\/2022\/probability-statistics-seminar-3\/","title":{"rendered":"Probability & Statistics Seminar"},"content":{"rendered":"

Date:\u00a0 Friday, November 25, 2022
\nTime:\u00a0 1:00 \u2013 2:00 p.m.
\nLocation: Zoom – https:\/\/carleton-ca.zoom.us\/j\/92672331592<\/a>
\nTitle:\u00a0 Evaluation\u00a0of latent-class mixed-effect models for trajectory clustering in complex data\u00a0sets through simulation studies
\nSpeaker: Dr. Yuan Fang, School of Public Health, Boston University
\n https:\/\/www.bu.edu\/sph\/profile\/yuan-fang\/<\/a><\/p>\n

Abstract:\u00a0 <\/strong>Clustering trajectories of disease\u00a0progression can help in understanding the variability of disease pathways.\u00a0Recently, a latent class mixed\u00a0effect models (LCMM) approach was developed and\u00a0has been applied to a wide range of progressive diseases to discover the underlying\u00a0patterns of deterioration in function. Often, biological and medical data have\u00a0complicated structures and substantial noise. Therefore, we use\u00a0simulation\u00a0studies to explore the ability of LCMMs to accurately classify individuals for\u00a0a wide range of noise and variability in trajectories and to\u00a0provide guidelines\u00a0for model specification when using this technique. Datasets were simulated from\u00a024 scenarios covering different data\u00a0structures and variability levels. We find\u00a0that LCMMs can reliably recover trajectory subgroups and model parameters even\u00a0for datasets that\u00a0contain unbalanced subgroups and in which subjects are\u00a0followed irregularly with different and short follow-up times, if the ratio of\u00a0the between-individual and total variance is relatively small. When the variability\u00a0is high in the data, LCMMs have difficulty classifying individuals into the\u00a0correct subgroups and thus should be applied with caution.<\/p>\n

This work is done in collaboration with Jiachen\u00a0Chen, Joanne M. Murabito, and Kathryn L. Lunetta.<\/p>\n

<\/p>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Date:\u00a0 Friday, November 25, 2022 Time:\u00a0 1:00 \u2013 2:00 p.m. Location: Zoom – https:\/\/carleton-ca.zoom.us\/j\/92672331592 Title:\u00a0 Evaluation\u00a0of latent-class mixed-effect models for trajectory clustering in complex data\u00a0sets through simulation studies Speaker: Dr. Yuan Fang, School of Public Health, Boston University https:\/\/www.bu.edu\/sph\/profile\/yuan-fang\/ Abstract:\u00a0 Clustering trajectories of disease\u00a0progression can help in understanding the variability of disease pathways.\u00a0Recently, a latent […]<\/p>\n","protected":false},"author":44,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[1,3],"tags":[],"yoast_head":"\nProbability & Statistics Seminar - 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