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The study evaluates whether modeling should impose an upper limit of quantification below the diagnostic limit of detection using data from randomized TB-PACTS trials and PanACEA MAMS-TB.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/analysis-of-time-to-positivity-data-in-tuberculosis-treatment-studies-identifying-a-new-limit-of-quantification/439683/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/analysis-of-time-to-positivity-data-in-tuberculosis-treatment-studies-identifying-a-new-limit-of-quantification/439683.png","ImageObject",300,407,{"name":92,"@type":93},"Chumphorn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-29",true,{"@type":101,"interactionType":102,"userInteractionCount":4},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"What does time-to-positivity (TTP) represent in MGIT testing?","Question",{"text":111,"@type":112},"TTP refers to the time until bacterial growth is detected in MGIT cultures. Samples observed for up to 42 days are then declared negative for tuberculosis if growth is not detected earlier.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"Why is TTP useful beyond diagnosis?",{"text":116,"@type":112},"TTP is increasingly used as a continuous biomarker. Changes in TTP can help compare bactericidal activity across different TB treatment regimens.",{"name":118,"@type":109,"acceptedAnswer":119},"What did the study find about measurements between 25 and 42 days?",{"text":120,"@type":112},"Less than 7.1% of weekly samples fell in that 25–42 day range, and modeling based on upper limits of quantification (ULOQM) around 25 or 30 days improved estimator precision and discrimination across regimens compared with using the LOD.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},439683,1790689864,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":128,"read_time":36},2336475401981,"https://ap-avatar.wpscdn.com/avatar/22000c94efd8d5204d?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786935347598174694","Author Manuscript Author Manuscript Author Manuscript Author Manuscript  \n\n| | HHS Public Access\u003Cbr>Author manuscript\u003Cbr>Int J Antimicrob Agents. Author manuscript; available in PMC 2026 January 05. |\n| --- | --- |\n\nPublished in final edited form as:  \nInt J Antimicrob Agents. 2025 February ; 65(2): 107404. doi:10.1016/j.ijantimicag.2024.107404 .  \nAnalysis of time-to-positivity data in tuberculosis treatment studies: Identifying a new limit of quantification  \nSuzanne M. Dufault a,b,* , Geraint R. Daviesc, Elin M. Svenssond,e, Derek J. Sloanf, Andrew D. McCallumg, Anu Patelh, Pieter Van Brantegemi, Paolo Dentij, Patrick P.J. Phillipsb, ha Division of Biostatistics, University of California, San Francisco, San Francisco, California, USA bUCSF Center for Tuberculosis, University of California, San Francisco, San Francisco, California, USA  \nc Institute of Infection and Global Health, University of Liverpool, Liverpool, UK d Department of Pharmacy, Radboud University Medical Center, Nijmegen, the Netherlands e Department of Pharmacy, Uppsala University, Uppsala, Sweden  \nfSchool of Medicine, University of St Andrews, St Andrews, UK  \ng Department of Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, Oxford, UK  \nh Division of Pulmonary and Critical Care Medicine, University of California, San Francisco, San Francisco, California, USA  \ni Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA  \nj Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, South Africa  \nAbstract  \nBackground: The BACTEC Mycobacteria Growth Indicator Tube (MGIT) machine is the standard globally for detecting viable mycobacteria in patients’ sputum. Samples are observed for no longer than 42 days, at which point the sample is declared ‘negative’ for tuberculosis (TB) .  \nThis time to detection of bacterial growth, referred to as time-to-positivity (TTP), is increasingly of interest, not solely as a diagnostic tool but also as a continuous biomarker wherein change in TTP can be used for comparing the bactericidal activity of different TB treatments. However, as a continuous measure, there are oddities in the distribution ofTTP values observed, particularly at higher values.  \nThis is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/))  \n*Corresponding author: Department of Epidemiology and Biostatistics, University of California at San Francisco, Mission Hall: Global Health and Clinical Sciences, Box 0560, 550 16th St, San Francisco, [CA 94143. suzanne.dufault@ucsf.edu](CA 94143. suzanne.dufault@ucsf.edu) (S.M. Dufault).  \nCompeting Interests: The authors declare no competing interests.  \nEthical Approval: Ethical approval was not required.  \nSequence Information: Not applicable.  \nAuthor Manuscript Author Manuscript Author Manuscript Author Manuscript  \nDufault et al. Page 2  \nMethods: We explored whether there is evidence to suggest setting an upper limit of quantification for modeling purposes (ULOQM) lower than the diagnostic limit of detection (LOD) using data from several TB-PACTS randomized clinical trials and PanACEA MAMS-TB.  \nResults: Across all trials, less than 7 . 1% of weekly samples returned TTP measurements between 25 and 42 days. Further, the relative absolute prediction error (%) was highest in this range. When modelling with ULOQM s of 25 and 30 days, estimator precision improved for 23 of 25 regimen-level slopes compared to models using the LOD. Discrimination between regimens based on Bayesian posteriors also improved.  \nConclusions: Although TTP measurements between 25 days and the diagnostic LOD may be important for diagnostic purposes, TTP values in this range may not contribute meaningfully to its use as a quantitative measure, particularly when assessing treatment response, and may lead to underpowered clinical trials.  \nKe","cbCaifF0puJuQtZT","https://ap.wps.com/l/cbCaifF0puJuQtZT","pdf",1174875,16,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# 1. Background\n## Mycobacterium tuberculosis and TB diagnostics\n## MGIT and the role of TTP","[{\"question\":\"What does time-to-positivity (TTP) represent in MGIT testing?\",\"answer\":\"TTP refers to the time until bacterial growth is detected in MGIT cultures. Samples observed for up to 42 days are then declared negative for tuberculosis if growth is not detected earlier.\"},{\"question\":\"Why is TTP useful beyond diagnosis?\",\"answer\":\"TTP is increasingly used as a continuous biomarker. Changes in TTP can help compare bactericidal activity across different TB treatment regimens.\"},{\"question\":\"What did the study find about measurements between 25 and 42 days?\",\"answer\":\"Less than 7.1% of weekly samples fell in that 25–42 day range, and modeling based on upper limits of quantification (ULOQM) around 25 or 30 days improved estimator precision and discrimination across regimens compared with using the LOD.\"}]","Analysis of time-to-positivity data in tuberculosis treatment studies - Identifying a new limit of quantification | PDF"]