Pharmacometric Generative Stochastic Modeling of Patient Reported Outcome Measures

Pharmacometric Generative Stochastic Modeling of Patient Reported Outcome Measures | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 June 2025 V1 Latest version Share on Pharmacometric Generative Stochastic Modeling of Patient Reported Outcome Measures Authors : Kuteesa R. Bisaso 0000-0001-7721-4005 [email protected] , Karyaburo R. Kadada , Karungi Bisaso , Jackson Mukonzo , and Ene Ette 0000-0003-3227-0454 Authors Info & Affiliations https://doi.org/10.22541/au.175024997.71977344/v1 147 views 115 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background/Objectives: Patient reported outcome measures (PROMs) capture the patient’s own perspective on their health, illness, and therapeutic effects on the illness. However, their analysis and interpretation is challenging due to their multidimensional nature, poor correlation with clinical and physiological outcomes, lack of standardized interpretation, and discrete nature of the data. We describe a generative stochastic modeling approach and show that it improves the pharmacometric characterization of multi-item PROMS. Methods: The Restricted Boltzmann Machine (RBM) modeling approach was described and used to model the relationship between efavirenz mid-dose concentrations, clinical variables (CD4 count and viral load) and time varying patient reported neuropsychological impairment symptoms. The model was used to derive a variable importance ranking for all the PROM items, clinical variables, and drug concentrations. Results: The model adequately characterizes the PROMs. Variable importance ranking reveals that mid-dose concentrations are not more predictive of post-baseline PROMs than clinical variables and baseline PROMs. Conclusions: Generative stochastic modeling with RBMs adequately characterizes PROMS and their relationship to other variables and drug concentrations, is readily adaptable to the pharmacometric workflow, and is able to generate individual-level disease progression trajectories using baseline variables. Supplementary Material File (manuscript.docx) Download 1.24 MB Information & Authors Information Version history V1 Version 1 18 June 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Kuteesa R. Bisaso 0000-0001-7721-4005 [email protected] Breakthrough Analytics Limited View all articles by this author Karyaburo R. Kadada Breakthrough Analytics Limited View all articles by this author Karungi Bisaso Breakthrough Analytics Limited View all articles by this author Jackson Mukonzo Makerere University College of Health Sciences View all articles by this author Ene Ette 0000-0003-3227-0454 Anoixis Corporation View all articles by this author Metrics & Citations Metrics Article Usage 147 views 115 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Kuteesa R. Bisaso, Karyaburo R. Kadada, Karungi Bisaso, et al. Pharmacometric Generative Stochastic Modeling of Patient Reported Outcome Measures. Authorea . 18 June 2025. DOI: https://doi.org/10.22541/au.175024997.71977344/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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