The Use of Computer Simulation Modeling to Estimate Complications in Patients with Type 2 Diabetes Mellitus: Comparative Validation of the Cornerstone Diabetes Simulation Model

Objective The objective of this study was to assess the validity of the Cornerstone Diabetes Simulation (CDS), a Microsoft Excel ® -based patient-level simulation for type 2 diabetes mellitus based on risk equations from the revised United Kingdom Prospective Diabetes Study Outcomes Model (UKPDS-OM2...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PharmacoEconomics - Open 2020-03, Vol.4 (1), p.37-44
Hauptverfasser: Su, Zhuo T., Bartelt-Hofer, Jose, Brown, Stephen, Lew, Elisheva, Sauriol, Luc, Annemans, Lieven, Grima, Daniel T.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 44
container_issue 1
container_start_page 37
container_title PharmacoEconomics - Open
container_volume 4
creator Su, Zhuo T.
Bartelt-Hofer, Jose
Brown, Stephen
Lew, Elisheva
Sauriol, Luc
Annemans, Lieven
Grima, Daniel T.
description Objective The objective of this study was to assess the validity of the Cornerstone Diabetes Simulation (CDS), a Microsoft Excel ® -based patient-level simulation for type 2 diabetes mellitus based on risk equations from the revised United Kingdom Prospective Diabetes Study Outcomes Model (UKPDS-OM2, also known as UKPDS 82). Methods Three levels of validation were conducted. Internal validation was assessed through independent review and model stress-testing. External validation was addressed by populating the CDS with baseline characteristics and treatment effects from three major diabetes clinical trials used in the Fifth Mount Hood Diabetes Challenge (MH5) for computer simulation models. Cross-validation of predicted outcomes was tested versus eight models that participated in the MH5. Simulated results were compared with observed clinical outcomes via the coefficient of determination ( R 2 ) for both the absolute risk of each clinical outcome and the difference in absolute risk between control and intervention arm in each trial. We ensured transparency of all model inputs and assumptions in reporting. Results The CDS could be used to predict 18 of 39 single and composite endpoints across the three trials. The model obtained an R 2 of 0.637 for predicted versus observed absolute risks, and an R 2 of 0.442 for predicted versus observed risk differences between control and intervention. Among the other eight models, only one obtained a higher R 2 value under both definitions, albeit based on only four predicted endpoints. Conclusions The CDS provides good predictions of diabetes-related complications when compared to observed trial outcomes and previously validated models. The model has value as a validated tool in cost-effectiveness evaluations.
doi_str_mv 10.1007/s41669-019-0156-x
format Article
fullrecord <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7018921</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A614578818</galeid><sourcerecordid>A614578818</sourcerecordid><originalsourceid>FETCH-LOGICAL-c509t-11f803aeba2b543cef84f3baf4a29d8d3ec655cec55a2037101525b38b4bb6243</originalsourceid><addsrcrecordid>eNp1Ustu1DAUjRCIVqUfwAZZYsMmxY88HBZI1bQ8pFYgMWVrOc7NjKvEHmyntH_FJ3JnUqZQCUVWLPucc-89Pln2ktETRmn9Nhasqpqcsu0qq_z2SXbIS9rkBS-Lp_t9xQ-y4xivKaVMSlbX7Hl2IBhieF0cZr-WayBXEYjvycKPmylBIN_sOA06We_Ipe9gsG5FkifnMdlRJ9gBB2t2iEisI19xCy5F8tOmNVnebYBwcmZ1CwkiuYRhsGmK73ZEHRB8A-S7Hmw3F8Haab2VDQ5CTN7BA_lxLy-yZ70eIhzf_4-yqw_ny8Wn_OLLx8-L04vc4NwpZ6yXVGhoNW_LQhjoZdGLVveF5k0nOwGmKksDpiw1p6Jm6CEvWyHbom0rXoij7P2su5naETqD4wU9qE1AC8Kd8tqqf2-cXauVv1E1-txwhgJv7gWC_zFBTGq00aAV2oGfouL4QBUTVAqEvn4EvfZTcDie4rWUkla8bhB1MqNWegBlXe-xrsGvg9EaNK23eH5asaJEEpNIYDPBBB9jgH7fPaNqmyE1Z0hhhtQ2Q-oWOa_-HnvP-JMYBPAZEPHKrSA89Pp_1d_H0tW1</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2788806279</pqid></control><display><type>article</type><title>The Use of Computer Simulation Modeling to Estimate Complications in Patients with Type 2 Diabetes Mellitus: Comparative Validation of the Cornerstone Diabetes Simulation Model</title><source>PubMed Central (Open Access)</source><source>SpringerOpen</source><source>Directory of Open Access Journals</source><source>EZB Electronic Journals Library</source><source>PubMed Central Open Access</source><creator>Su, Zhuo T. ; Bartelt-Hofer, Jose ; Brown, Stephen ; Lew, Elisheva ; Sauriol, Luc ; Annemans, Lieven ; Grima, Daniel T.</creator><creatorcontrib>Su, Zhuo T. ; Bartelt-Hofer, Jose ; Brown, Stephen ; Lew, Elisheva ; Sauriol, Luc ; Annemans, Lieven ; Grima, Daniel T.</creatorcontrib><description>Objective The objective of this study was to assess the validity of the Cornerstone Diabetes Simulation (CDS), a Microsoft Excel ® -based patient-level simulation for type 2 diabetes mellitus based on risk equations from the revised United Kingdom Prospective Diabetes Study Outcomes Model (UKPDS-OM2, also known as UKPDS 82). Methods Three levels of validation were conducted. Internal validation was assessed through independent review and model stress-testing. External validation was addressed by populating the CDS with baseline characteristics and treatment effects from three major diabetes clinical trials used in the Fifth Mount Hood Diabetes Challenge (MH5) for computer simulation models. Cross-validation of predicted outcomes was tested versus eight models that participated in the MH5. Simulated results were compared with observed clinical outcomes via the coefficient of determination ( R 2 ) for both the absolute risk of each clinical outcome and the difference in absolute risk between control and intervention arm in each trial. We ensured transparency of all model inputs and assumptions in reporting. Results The CDS could be used to predict 18 of 39 single and composite endpoints across the three trials. The model obtained an R 2 of 0.637 for predicted versus observed absolute risks, and an R 2 of 0.442 for predicted versus observed risk differences between control and intervention. Among the other eight models, only one obtained a higher R 2 value under both definitions, albeit based on only four predicted endpoints. Conclusions The CDS provides good predictions of diabetes-related complications when compared to observed trial outcomes and previously validated models. The model has value as a validated tool in cost-effectiveness evaluations.</description><identifier>ISSN: 2509-4262</identifier><identifier>EISSN: 2509-4254</identifier><identifier>DOI: 10.1007/s41669-019-0156-x</identifier><identifier>PMID: 31254274</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Blood pressure ; Body mass index ; Cardiovascular disease ; Care and treatment ; Clinical trials ; Comparative analysis ; Complications and side effects ; Computer simulation ; Cost analysis ; Decision making ; Diabetes ; Diabetes mellitus ; Diabetes research ; Diabetes therapy ; Economic models ; Intervention ; Medical research ; Medicine ; Medicine &amp; Public Health ; Monte Carlo simulation ; Mortality ; Original ; Original Research Article ; Patients ; Pharmaceutical industry ; Pharmacoeconomics and Health Outcomes ; Prognosis ; Risk factors ; Type 2 diabetes ; Validity</subject><ispartof>PharmacoEconomics - Open, 2020-03, Vol.4 (1), p.37-44</ispartof><rights>The Author(s) 2019</rights><rights>COPYRIGHT 2020 Springer</rights><rights>The Author(s) 2019. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c509t-11f803aeba2b543cef84f3baf4a29d8d3ec655cec55a2037101525b38b4bb6243</citedby><cites>FETCH-LOGICAL-c509t-11f803aeba2b543cef84f3baf4a29d8d3ec655cec55a2037101525b38b4bb6243</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7018921/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7018921/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,27929,27930,41125,42194,51581,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31254274$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Su, Zhuo T.</creatorcontrib><creatorcontrib>Bartelt-Hofer, Jose</creatorcontrib><creatorcontrib>Brown, Stephen</creatorcontrib><creatorcontrib>Lew, Elisheva</creatorcontrib><creatorcontrib>Sauriol, Luc</creatorcontrib><creatorcontrib>Annemans, Lieven</creatorcontrib><creatorcontrib>Grima, Daniel T.</creatorcontrib><title>The Use of Computer Simulation Modeling to Estimate Complications in Patients with Type 2 Diabetes Mellitus: Comparative Validation of the Cornerstone Diabetes Simulation Model</title><title>PharmacoEconomics - Open</title><addtitle>PharmacoEconomics Open</addtitle><addtitle>Pharmacoecon Open</addtitle><description>Objective The objective of this study was to assess the validity of the Cornerstone Diabetes Simulation (CDS), a Microsoft Excel ® -based patient-level simulation for type 2 diabetes mellitus based on risk equations from the revised United Kingdom Prospective Diabetes Study Outcomes Model (UKPDS-OM2, also known as UKPDS 82). Methods Three levels of validation were conducted. Internal validation was assessed through independent review and model stress-testing. External validation was addressed by populating the CDS with baseline characteristics and treatment effects from three major diabetes clinical trials used in the Fifth Mount Hood Diabetes Challenge (MH5) for computer simulation models. Cross-validation of predicted outcomes was tested versus eight models that participated in the MH5. Simulated results were compared with observed clinical outcomes via the coefficient of determination ( R 2 ) for both the absolute risk of each clinical outcome and the difference in absolute risk between control and intervention arm in each trial. We ensured transparency of all model inputs and assumptions in reporting. Results The CDS could be used to predict 18 of 39 single and composite endpoints across the three trials. The model obtained an R 2 of 0.637 for predicted versus observed absolute risks, and an R 2 of 0.442 for predicted versus observed risk differences between control and intervention. Among the other eight models, only one obtained a higher R 2 value under both definitions, albeit based on only four predicted endpoints. Conclusions The CDS provides good predictions of diabetes-related complications when compared to observed trial outcomes and previously validated models. The model has value as a validated tool in cost-effectiveness evaluations.</description><subject>Blood pressure</subject><subject>Body mass index</subject><subject>Cardiovascular disease</subject><subject>Care and treatment</subject><subject>Clinical trials</subject><subject>Comparative analysis</subject><subject>Complications and side effects</subject><subject>Computer simulation</subject><subject>Cost analysis</subject><subject>Decision making</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes research</subject><subject>Diabetes therapy</subject><subject>Economic models</subject><subject>Intervention</subject><subject>Medical research</subject><subject>Medicine</subject><subject>Medicine &amp; Public Health</subject><subject>Monte Carlo simulation</subject><subject>Mortality</subject><subject>Original</subject><subject>Original Research Article</subject><subject>Patients</subject><subject>Pharmaceutical industry</subject><subject>Pharmacoeconomics and Health Outcomes</subject><subject>Prognosis</subject><subject>Risk factors</subject><subject>Type 2 diabetes</subject><subject>Validity</subject><issn>2509-4262</issn><issn>2509-4254</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1Ustu1DAUjRCIVqUfwAZZYsMmxY88HBZI1bQ8pFYgMWVrOc7NjKvEHmyntH_FJ3JnUqZQCUVWLPucc-89Pln2ktETRmn9Nhasqpqcsu0qq_z2SXbIS9rkBS-Lp_t9xQ-y4xivKaVMSlbX7Hl2IBhieF0cZr-WayBXEYjvycKPmylBIN_sOA06We_Ipe9gsG5FkifnMdlRJ9gBB2t2iEisI19xCy5F8tOmNVnebYBwcmZ1CwkiuYRhsGmK73ZEHRB8A-S7Hmw3F8Haab2VDQ5CTN7BA_lxLy-yZ70eIhzf_4-yqw_ny8Wn_OLLx8-L04vc4NwpZ6yXVGhoNW_LQhjoZdGLVveF5k0nOwGmKksDpiw1p6Jm6CEvWyHbom0rXoij7P2su5naETqD4wU9qE1AC8Kd8tqqf2-cXauVv1E1-txwhgJv7gWC_zFBTGq00aAV2oGfouL4QBUTVAqEvn4EvfZTcDie4rWUkla8bhB1MqNWegBlXe-xrsGvg9EaNK23eH5asaJEEpNIYDPBBB9jgH7fPaNqmyE1Z0hhhtQ2Q-oWOa_-HnvP-JMYBPAZEPHKrSA89Pp_1d_H0tW1</recordid><startdate>20200301</startdate><enddate>20200301</enddate><creator>Su, Zhuo T.</creator><creator>Bartelt-Hofer, Jose</creator><creator>Brown, Stephen</creator><creator>Lew, Elisheva</creator><creator>Sauriol, Luc</creator><creator>Annemans, Lieven</creator><creator>Grima, Daniel T.</creator><general>Springer International Publishing</general><general>Springer</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IAO</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88C</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>L.-</scope><scope>M0C</scope><scope>M0S</scope><scope>M0T</scope><scope>PIMPY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20200301</creationdate><title>The Use of Computer Simulation Modeling to Estimate Complications in Patients with Type 2 Diabetes Mellitus: Comparative Validation of the Cornerstone Diabetes Simulation Model</title><author>Su, Zhuo T. ; Bartelt-Hofer, Jose ; Brown, Stephen ; Lew, Elisheva ; Sauriol, Luc ; Annemans, Lieven ; Grima, Daniel T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c509t-11f803aeba2b543cef84f3baf4a29d8d3ec655cec55a2037101525b38b4bb6243</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Blood pressure</topic><topic>Body mass index</topic><topic>Cardiovascular disease</topic><topic>Care and treatment</topic><topic>Clinical trials</topic><topic>Comparative analysis</topic><topic>Complications and side effects</topic><topic>Computer simulation</topic><topic>Cost analysis</topic><topic>Decision making</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes research</topic><topic>Diabetes therapy</topic><topic>Economic models</topic><topic>Intervention</topic><topic>Medical research</topic><topic>Medicine</topic><topic>Medicine &amp; Public Health</topic><topic>Monte Carlo simulation</topic><topic>Mortality</topic><topic>Original</topic><topic>Original Research Article</topic><topic>Patients</topic><topic>Pharmaceutical industry</topic><topic>Pharmacoeconomics and Health Outcomes</topic><topic>Prognosis</topic><topic>Risk factors</topic><topic>Type 2 diabetes</topic><topic>Validity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Su, Zhuo T.</creatorcontrib><creatorcontrib>Bartelt-Hofer, Jose</creatorcontrib><creatorcontrib>Brown, Stephen</creatorcontrib><creatorcontrib>Lew, Elisheva</creatorcontrib><creatorcontrib>Sauriol, Luc</creatorcontrib><creatorcontrib>Annemans, Lieven</creatorcontrib><creatorcontrib>Grima, Daniel T.</creatorcontrib><collection>SpringerOpen</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale Academic OneFile Select</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Health &amp; Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global (ProQuest)</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Health Management Database (Proquest)</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>PharmacoEconomics - Open</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Su, Zhuo T.</au><au>Bartelt-Hofer, Jose</au><au>Brown, Stephen</au><au>Lew, Elisheva</au><au>Sauriol, Luc</au><au>Annemans, Lieven</au><au>Grima, Daniel T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Use of Computer Simulation Modeling to Estimate Complications in Patients with Type 2 Diabetes Mellitus: Comparative Validation of the Cornerstone Diabetes Simulation Model</atitle><jtitle>PharmacoEconomics - Open</jtitle><stitle>PharmacoEconomics Open</stitle><addtitle>Pharmacoecon Open</addtitle><date>2020-03-01</date><risdate>2020</risdate><volume>4</volume><issue>1</issue><spage>37</spage><epage>44</epage><pages>37-44</pages><issn>2509-4262</issn><eissn>2509-4254</eissn><abstract>Objective The objective of this study was to assess the validity of the Cornerstone Diabetes Simulation (CDS), a Microsoft Excel ® -based patient-level simulation for type 2 diabetes mellitus based on risk equations from the revised United Kingdom Prospective Diabetes Study Outcomes Model (UKPDS-OM2, also known as UKPDS 82). Methods Three levels of validation were conducted. Internal validation was assessed through independent review and model stress-testing. External validation was addressed by populating the CDS with baseline characteristics and treatment effects from three major diabetes clinical trials used in the Fifth Mount Hood Diabetes Challenge (MH5) for computer simulation models. Cross-validation of predicted outcomes was tested versus eight models that participated in the MH5. Simulated results were compared with observed clinical outcomes via the coefficient of determination ( R 2 ) for both the absolute risk of each clinical outcome and the difference in absolute risk between control and intervention arm in each trial. We ensured transparency of all model inputs and assumptions in reporting. Results The CDS could be used to predict 18 of 39 single and composite endpoints across the three trials. The model obtained an R 2 of 0.637 for predicted versus observed absolute risks, and an R 2 of 0.442 for predicted versus observed risk differences between control and intervention. Among the other eight models, only one obtained a higher R 2 value under both definitions, albeit based on only four predicted endpoints. Conclusions The CDS provides good predictions of diabetes-related complications when compared to observed trial outcomes and previously validated models. The model has value as a validated tool in cost-effectiveness evaluations.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>31254274</pmid><doi>10.1007/s41669-019-0156-x</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2509-4262
ispartof PharmacoEconomics - Open, 2020-03, Vol.4 (1), p.37-44
issn 2509-4262
2509-4254
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7018921
source PubMed Central (Open Access); SpringerOpen; Directory of Open Access Journals; EZB Electronic Journals Library; PubMed Central Open Access
subjects Blood pressure
Body mass index
Cardiovascular disease
Care and treatment
Clinical trials
Comparative analysis
Complications and side effects
Computer simulation
Cost analysis
Decision making
Diabetes
Diabetes mellitus
Diabetes research
Diabetes therapy
Economic models
Intervention
Medical research
Medicine
Medicine & Public Health
Monte Carlo simulation
Mortality
Original
Original Research Article
Patients
Pharmaceutical industry
Pharmacoeconomics and Health Outcomes
Prognosis
Risk factors
Type 2 diabetes
Validity
title The Use of Computer Simulation Modeling to Estimate Complications in Patients with Type 2 Diabetes Mellitus: Comparative Validation of the Cornerstone Diabetes Simulation Model
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-13T16%3A46%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20Use%20of%20Computer%20Simulation%20Modeling%20to%20Estimate%20Complications%20in%20Patients%20with%20Type%202%20Diabetes%20Mellitus:%20Comparative%20Validation%20of%20the%20Cornerstone%20Diabetes%20Simulation%20Model&rft.jtitle=PharmacoEconomics%20-%20Open&rft.au=Su,%20Zhuo%20T.&rft.date=2020-03-01&rft.volume=4&rft.issue=1&rft.spage=37&rft.epage=44&rft.pages=37-44&rft.issn=2509-4262&rft.eissn=2509-4254&rft_id=info:doi/10.1007/s41669-019-0156-x&rft_dat=%3Cgale_pubme%3EA614578818%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2788806279&rft_id=info:pmid/31254274&rft_galeid=A614578818&rfr_iscdi=true