Defining treatment regimens and lines of therapy using real-world data in oncology.
Retrospective observational research relies on databases that do not routinely record lines of therapy or reasons for treatment change. Standardized approaches to estimate lines of therapy were developed and evaluated in this study. A number of rules were developed, assumptions varied and macros dev...
| Publicado en: | Future Oncology Vol. 17; no. 15; pp. 1865 - 1878 |
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| Autores principales: | , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
May2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149835614&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149835614 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14796694 3CMQ jtl: Future Oncology issn: 14796694 maglogo: N pubinfo: dt: May2021 vid: 17 iid: 15 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 149835614 149835614 NLM33629590 149835614 10.2217/fon-2020-1041 NLM33629590 149835614 ppf: 1865 ppct: 13 formats: tig: atl: Defining treatment regimens and lines of therapy using real-world data in oncology. aug: au: Hess, Lisa M Li, Xiaohong Wu, Yixun Goodloe, Robert J Cui, Zhanglin Lin affil: Eli Lilly & Company, Indianapolis, IN 46285, USA sug: subj: Colorectal Neoplasms Drug Therapy Antineoplastic Agents, Combined Therapeutic Use Stomach Neoplasms Drug Therapy Oncology Standards Lung Neoplasms Drug Therapy Software Data Collection Standards Resource Databases Standards Algorithms Resource Databases Oncology Statistics and Numerical Data Retrospective Design Human ab: Retrospective observational research relies on databases that do not routinely record lines of therapy or reasons for treatment change. Standardized approaches to estimate lines of therapy were developed and evaluated in this study. A number of rules were developed, assumptions varied and macros developed to apply to large datasets. Results were investigated in an iterative process to refine line of therapy algorithms in three different cancers (lung, colorectal and gastric). Three primary factors were evaluated and included in the estimation of lines of therapy in oncology: defining a treatment regimen, addition/removal of drugs and gap periods. Algorithms and associated Statistical Analysis Software (SAS®) macros for line of therapy identification are provided to facilitate and standardize the use of real-world databases for oncology research. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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