Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination

Background The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to estimate the joint causal effect of two time-dependent nonrandomized treatments on survival among HIV-positive subjects. Neverthele...

Full description

Saved in:

Bibliographic Details
Published in:	BMC medical research methodology Vol. 17; no. 1; pp. 160 - 11
Main Authors:	Lusivika-Nzinga, Clovis, Selinger-Leneman, Hana, Grabar, Sophie, Costagliola, Dominique, Carrat, Fabrice
Format:	Journal Article
Language:	English
Published:	London BioMed Central 04.12.2017 BioMed Central Ltd BMC
Subjects:	Algorithms Analysis Anus Neoplasms - chemically induced Anus Neoplasms - epidemiology Causal inference CD4 Lymphocyte Count Data analysis Female Health Sciences Highly active antiretroviral therapy HIV Infections - drug therapy HIV Infections - epidemiology HIV Infections - immunology HIV Protease Inhibitors - adverse effects HIV Protease Inhibitors - therapeutic use Humans Life Sciences Longitudinal data Male Marginal structural models Medicine Medicine & Public Health Multitherapy Pharmaceutical sciences Proportional Hazards Models Protease inhibitors Proteases Research Article Santé publique et épidémiologie Statistical Theory and Methods statistics and modelling Statistics for Life Sciences Theory of Medicine/Bioethics Time-dependent confounding Treatment Outcome Causal inference Time-dependent confounding Longitudinal data Marginal structural models Multitherapy
ISSN:	1471-2288, 1471-2288
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Background The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to estimate the joint causal effect of two time-dependent nonrandomized treatments on survival among HIV-positive subjects. Nevertheless, Cox-MSM performance in the case of multiple treatments has not been fully explored under different degree of time-dependent confounding for treatments or in case of interaction between treatments. We aimed to evaluate and compare the performance of the marginal structural Cox model (Cox-MSM) to the standard Cox model in estimating the treatment effect in the case of multiple treatments under different scenarios of time-dependent confounding and when an interaction between treatment effects is present. Methods We specified a Cox-MSM with two treatments including an interaction term for situations where an adverse event might be caused by two treatments taken simultaneously but not by each treatment taken alone. We simulated longitudinal data with two treatments and a time-dependent confounder affected by one or the two treatments. To fit the Cox-MSM, we used the inverse probability weighting method. We illustrated the method to evaluate the specific effect of protease inhibitors combined (or not) to other antiretroviral medications on the anal cancer risk in HIV-infected individuals, with CD4 cell count as time-dependent confounder. Results Overall, Cox-MSM performed better than the standard Cox model. Furthermore, we showed that estimates were unbiased when an interaction term was included in the model. Conclusion Cox-MSM may be used for accurately estimating causal individual and joined treatment effects from a combination therapy in presence of time-dependent confounding provided that an interaction term is estimated.
AbstractList	The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to estimate the joint causal effect of two time-dependent nonrandomized treatments on survival among HIV-positive subjects. Nevertheless, Cox-MSM performance in the case of multiple treatments has not been fully explored under different degree of time-dependent confounding for treatments or in case of interaction between treatments. We aimed to evaluate and compare the performance of the marginal structural Cox model (Cox-MSM) to the standard Cox model in estimating the treatment effect in the case of multiple treatments under different scenarios of time-dependent confounding and when an interaction between treatment effects is present. We specified a Cox-MSM with two treatments including an interaction term for situations where an adverse event might be caused by two treatments taken simultaneously but not by each treatment taken alone. We simulated longitudinal data with two treatments and a time-dependent confounder affected by one or the two treatments. To fit the Cox-MSM, we used the inverse probability weighting method. We illustrated the method to evaluate the specific effect of protease inhibitors combined (or not) to other antiretroviral medications on the anal cancer risk in HIV-infected individuals, with CD4 cell count as time-dependent confounder. Overall, Cox-MSM performed better than the standard Cox model. Furthermore, we showed that estimates were unbiased when an interaction term was included in the model. Cox-MSM may be used for accurately estimating causal individual and joined treatment effects from a combination therapy in presence of time-dependent confounding provided that an interaction term is estimated. Background The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to estimate the joint causal effect of two time-dependent nonrandomized treatments on survival among HIV-positive subjects. Nevertheless, Cox-MSM performance in the case of multiple treatments has not been fully explored under different degree of time-dependent confounding for treatments or in case of interaction between treatments. We aimed to evaluate and compare the performance of the marginal structural Cox model (Cox-MSM) to the standard Cox model in estimating the treatment effect in the case of multiple treatments under different scenarios of time-dependent confounding and when an interaction between treatment effects is present. Methods We specified a Cox-MSM with two treatments including an interaction term for situations where an adverse event might be caused by two treatments taken simultaneously but not by each treatment taken alone. We simulated longitudinal data with two treatments and a time-dependent confounder affected by one or the two treatments. To fit the Cox-MSM, we used the inverse probability weighting method. We illustrated the method to evaluate the specific effect of protease inhibitors combined (or not) to other antiretroviral medications on the anal cancer risk in HIV-infected individuals, with CD4 cell count as time-dependent confounder. Results Overall, Cox-MSM performed better than the standard Cox model. Furthermore, we showed that estimates were unbiased when an interaction term was included in the model. Conclusion Cox-MSM may be used for accurately estimating causal individual and joined treatment effects from a combination therapy in presence of time-dependent confounding provided that an interaction term is estimated. The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to estimate the joint causal effect of two time-dependent nonrandomized treatments on survival among HIV-positive subjects. Nevertheless, Cox-MSM performance in the case of multiple treatments has not been fully explored under different degree of time-dependent confounding for treatments or in case of interaction between treatments. We aimed to evaluate and compare the performance of the marginal structural Cox model (Cox-MSM) to the standard Cox model in estimating the treatment effect in the case of multiple treatments under different scenarios of time-dependent confounding and when an interaction between treatment effects is present.BACKGROUNDThe Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to estimate the joint causal effect of two time-dependent nonrandomized treatments on survival among HIV-positive subjects. Nevertheless, Cox-MSM performance in the case of multiple treatments has not been fully explored under different degree of time-dependent confounding for treatments or in case of interaction between treatments. We aimed to evaluate and compare the performance of the marginal structural Cox model (Cox-MSM) to the standard Cox model in estimating the treatment effect in the case of multiple treatments under different scenarios of time-dependent confounding and when an interaction between treatment effects is present.We specified a Cox-MSM with two treatments including an interaction term for situations where an adverse event might be caused by two treatments taken simultaneously but not by each treatment taken alone. We simulated longitudinal data with two treatments and a time-dependent confounder affected by one or the two treatments. To fit the Cox-MSM, we used the inverse probability weighting method. We illustrated the method to evaluate the specific effect of protease inhibitors combined (or not) to other antiretroviral medications on the anal cancer risk in HIV-infected individuals, with CD4 cell count as time-dependent confounder.METHODSWe specified a Cox-MSM with two treatments including an interaction term for situations where an adverse event might be caused by two treatments taken simultaneously but not by each treatment taken alone. We simulated longitudinal data with two treatments and a time-dependent confounder affected by one or the two treatments. To fit the Cox-MSM, we used the inverse probability weighting method. We illustrated the method to evaluate the specific effect of protease inhibitors combined (or not) to other antiretroviral medications on the anal cancer risk in HIV-infected individuals, with CD4 cell count as time-dependent confounder.Overall, Cox-MSM performed better than the standard Cox model. Furthermore, we showed that estimates were unbiased when an interaction term was included in the model.RESULTSOverall, Cox-MSM performed better than the standard Cox model. Furthermore, we showed that estimates were unbiased when an interaction term was included in the model.Cox-MSM may be used for accurately estimating causal individual and joined treatment effects from a combination therapy in presence of time-dependent confounding provided that an interaction term is estimated.CONCLUSIONCox-MSM may be used for accurately estimating causal individual and joined treatment effects from a combination therapy in presence of time-dependent confounding provided that an interaction term is estimated. Abstract Background The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to estimate the joint causal effect of two time-dependent nonrandomized treatments on survival among HIV-positive subjects. Nevertheless, Cox-MSM performance in the case of multiple treatments has not been fully explored under different degree of time-dependent confounding for treatments or in case of interaction between treatments. We aimed to evaluate and compare the performance of the marginal structural Cox model (Cox-MSM) to the standard Cox model in estimating the treatment effect in the case of multiple treatments under different scenarios of time-dependent confounding and when an interaction between treatment effects is present. Methods We specified a Cox-MSM with two treatments including an interaction term for situations where an adverse event might be caused by two treatments taken simultaneously but not by each treatment taken alone. We simulated longitudinal data with two treatments and a time-dependent confounder affected by one or the two treatments. To fit the Cox-MSM, we used the inverse probability weighting method. We illustrated the method to evaluate the specific effect of protease inhibitors combined (or not) to other antiretroviral medications on the anal cancer risk in HIV-infected individuals, with CD4 cell count as time-dependent confounder. Results Overall, Cox-MSM performed better than the standard Cox model. Furthermore, we showed that estimates were unbiased when an interaction term was included in the model. Conclusion Cox-MSM may be used for accurately estimating causal individual and joined treatment effects from a combination therapy in presence of time-dependent confounding provided that an interaction term is estimated. BACKGROUND:The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to estimate the joint causal effect of two time-dependent nonrandomized treatments on survival among HIV-positive subjects. Nevertheless, Cox-MSM performance in the case of multiple treatments has not been fully explored under different degree of time-dependent confounding for treatments or in case of interaction between treatments. We aimed to evaluate and compare the performance of the marginal structural Cox model (Cox-MSM) to the standard Cox model in estimating the treatment effect in the case of multiple treatments under different scenarios of time-dependent confounding and when an interaction between treatment effects is present.METHODS:We specified a Cox-MSM with two treatments including an interaction term for situations where an adverse event might be caused by two treatments taken simultaneously but not by each treatment taken alone. We simulated longitudinal data with two treatments and a time-dependent confounder affected by one or the two treatments. To fit the Cox-MSM, we used the inverse probability weighting method. We illustrated the method to evaluate the specific effect of protease inhibitors combined (or not) to other antiretroviral medications on the anal cancer risk in HIV-infected individuals, with CD4 cell count as time-dependent confounder.RESULTS:Overall, Cox-MSM performed better than the standard Cox model. Furthermore, we showed that estimates were unbiased when an interaction term was included in the model.CONCLUSION:Cox-MSM may be used for accurately estimating causal individual and joined treatment effects from a combination therapy in presence of time-dependent confounding provided that an interaction term is estimated.
ArticleNumber	160
Audience	Academic
Author	Selinger-Leneman, Hana Grabar, Sophie Carrat, Fabrice Lusivika-Nzinga, Clovis Costagliola, Dominique
Author_xml	– sequence: 1 givenname: Clovis surname: Lusivika-Nzinga fullname: Lusivika-Nzinga, Clovis organization: Sorbonne Universités, INSERM, UPMC Université Paris 06, Institut Pierre Louis d’épidémiologie et de Santé Publique (IPLESP UMRS 1136) – sequence: 2 givenname: Hana surname: Selinger-Leneman fullname: Selinger-Leneman, Hana organization: Sorbonne Universités, INSERM, UPMC Université Paris 06, Institut Pierre Louis d’épidémiologie et de Santé Publique (IPLESP UMRS 1136) – sequence: 3 givenname: Sophie surname: Grabar fullname: Grabar, Sophie organization: Sorbonne Universités, INSERM, UPMC Université Paris 06, Institut Pierre Louis d’épidémiologie et de Santé Publique (IPLESP UMRS 1136), Unité de Biostatistique et d’épidémiologie Groupe hospitalier Cochin Broca Hôtel-Dieu, Assistance Publique Hôpitaux de Paris (AP-HP), and Université Paris Descartes, Sorbonne Paris Cité – sequence: 4 givenname: Dominique surname: Costagliola fullname: Costagliola, Dominique organization: Sorbonne Universités, INSERM, UPMC Université Paris 06, Institut Pierre Louis d’épidémiologie et de Santé Publique (IPLESP UMRS 1136) – sequence: 5 givenname: Fabrice orcidid: 0000-0002-8672-7918 surname: Carrat fullname: Carrat, Fabrice email: fabrice.carrat@iplesp.upmc.fr organization: Sorbonne Universités, INSERM, UPMC Université Paris 06, Institut Pierre Louis d’épidémiologie et de Santé Publique (IPLESP UMRS 1136), Unité de Santé Publique, Hôpital Saint-Antoine, Assistance Publique-Hôpitaux de Paris
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29202691$$D View this record in MEDLINE/PubMed https://inserm.hal.science/inserm-02310904$$DView record in HAL
BookMark	eNp9kk9v1DAQxSNURP_AB-CCInHhQIodO3FyQVpVQCutBAc4W449znqV2MVOVnDjozO7KVW3QpUPcezfezNjvfPsxAcPWfaakktKm_pDomUjeEGoKAhnvKDPsjPKBS3KsmlOHuxPs_OUtgTBhtUvstOyLUlZt_Qs-_MNog1xVF5DHmw-bSAfVeydV0OepjjraY641eFXPgYDQ450Dmlyo5qc73Pnjds5MyOjvMm3wXkwOVgLekoHxwhqGsHjX-924FGBbmOHFSYX_MvsuVVDgld334vsx-dP36-ui_XXLzdXq3Wha1pORdtoZgVTqqkqxhsBpOKMiFpYIUhtmalb0KBpVxsC0CJBDGlJ1yjDVYvARXaz-JqgtvI2Yv_xtwzKycNBiL1UcXJ6ACkULXlHqk4rwVshWsoAbGkrSmvDBEevj4vX7dyNYDQOh290ZHp8491G9mEnK0ErdEGD94vB5pHserWWzieIoyQlozgB3-3xd3f1Yvg54-PL0SUNw6A8hDlJ2gpGaNu0BNG3C9ornMR5G7ABvcflquI1qzEn-wEu_0PhMjA6jSGzDs-PBG8eTnzf878gIUAXQMeQUgR7j1Ai92GVS1glZlDuwyr3GvFIo910CAV244YnleWiTFjF9xDlNswRI5ueEP0Fr8f9sw
CitedBy_id	crossref_primary_10_1109_ACCESS_2018_2866049 crossref_primary_10_1088_1755_1315_332_3_032005 crossref_primary_10_1016_j_cgh_2022_06_011 crossref_primary_10_1016_j_xkme_2024_100896 crossref_primary_10_1002_ijc_32730 crossref_primary_10_1080_24709360_2023_2171537 crossref_primary_10_1136_gutjnl_2019_318932 crossref_primary_10_1016_j_ssci_2024_106562 crossref_primary_10_1186_s41512_021_00092_9 crossref_primary_10_1016_j_drugalcdep_2020_108137 crossref_primary_10_1007_s10742_020_00228_2 crossref_primary_10_1007_s12094_024_03459_8 crossref_primary_10_1097_QAI_0000000000003436
Cites_doi	10.1097/00001648-200009000-00011 10.1002/sim.5317 10.1002/bimj.201300159 10.1016/j.jclinepi.2013.03.020 10.1097/QAI.0000000000000523 10.1097/EDE.0b013e3181ba333c 10.1198/016214501753168154 10.1023/A:1005285815569 10.1002/sim.5753 10.1200/JCO.2012.44.5486 10.1002/sim.5686 10.1007/s10928-005-9002-0 10.1177/0962280213505804 10.1093/aje/kwk116 10.1080/03610918.2016.1248574 10.1097/01.ede.0000128401.55545.c6 10.1002/sim.5994 10.1007/s10985-013-9255-7 10.1097/QAD.0b013e32835935b3 10.1097/01.ede.0000135174.63482.43 10.1080/01621459.2013.872650 10.1201/9781420011579.ch23 10.1097/EDE.0b013e31824d1ccb 10.1002/sim.7266 10.1093/aje/kwg206 10.1016/0270-0255(86)90088-6 10.1111/j.1541-0420.2005.00377.x 10.2202/1557-4679.1208 10.1093/ije/dyu002 10.1007/s10985-009-9135-3 10.1093/aje/kwn164 10.1002/sim.5705 10.1093/aje/kwh131 10.1016/S1470-2045(09)70282-7 10.1002/sim.5472 10.1080/01621459.1994.10476818 10.1177/0962280216668554
ContentType	Journal Article
Copyright	The Author(s). 2017 COPYRIGHT 2017 BioMed Central Ltd. Distributed under a Creative Commons Attribution 4.0 International License
Copyright_xml	– notice: The Author(s). 2017 – notice: COPYRIGHT 2017 BioMed Central Ltd. – notice: Distributed under a Creative Commons Attribution 4.0 International License
DBID	C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 1XC VOOES 5PM DOA
DOI	10.1186/s12874-017-0434-1
DatabaseName	Springer Nature OA Free Journals (WRLC) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access) PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic
DatabaseTitleList	MEDLINE MEDLINE - Academic
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine
EISSN	1471-2288
EndPage	11
ExternalDocumentID	oai_doaj_org_article_7a124b05bca74977913eef2f5116d374 PMC5715511 oai:HAL:inserm-02310904v1 A546364344 29202691 10_1186_s12874_017_0434_1
Genre	Journal Article
GrantInformation_xml	– fundername: Agence Nationale de Recherches sur le Sida et les Hepatites Virales funderid: http://dx.doi.org/10.13039/501100003323 – fundername: ;
GroupedDBID	--- 0R~ 23N 2WC 53G 5VS 6J9 6PF 7X7 88E 8FI 8FJ AAFWJ AAJSJ AASML AAWTL ABDBF ABUWG ACGFO ACGFS ACIHN ACUHS ADBBV ADRAZ ADUKV AEAQA AENEX AFKRA AFPKN AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BCNDV BENPR BFQNJ BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 E3Z EAD EAP EAS EBD EBLON EBS EJD EMB EMK EMOBN ESX F5P FYUFA GROUPED_DOAJ GX1 H13 HMCUK HYE IAO IHR INH INR IPNFZ ITC KQ8 M1P M48 MK0 M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO PUEGO RBZ RIG RNS ROL RPM RSV SMD SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB AAYXX AFFHD CITATION -A0 3V. ACRMQ ADINQ ALIPV C24 CGR CUY CVF ECM EIF NPM 7X8 1XC VOOES 5PM
ID	FETCH-LOGICAL-c612t-98c3f73aa8553487e05430767f7706f3d69ecec1b6d0ee94870d090b8ad4a96f3
IEDL.DBID	RSV
ISICitedReferencesCount	11
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000417509000002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1471-2288
IngestDate	Fri Oct 03 12:48:24 EDT 2025 Tue Nov 04 02:00:17 EST 2025 Tue Oct 14 20:01:58 EDT 2025 Sun Nov 09 10:22:07 EST 2025 Tue Nov 11 10:02:33 EST 2025 Tue Nov 04 18:03:30 EST 2025 Thu Jan 02 22:39:11 EST 2025 Tue Nov 18 21:38:59 EST 2025 Sat Nov 29 06:38:55 EST 2025 Sat Sep 06 07:35:29 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	Causal inference Time-dependent confounding Longitudinal data Marginal structural models Multitherapy
Language	English
License	Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c612t-98c3f73aa8553487e05430767f7706f3d69ecec1b6d0ee94870d090b8ad4a96f3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 PMCID: PMC5715511
ORCID	0000-0002-8672-7918 0000-0003-4457-4228
OpenAccessLink	https://link.springer.com/10.1186/s12874-017-0434-1
PMID	29202691
PQID	1973019890
PQPubID	23479
PageCount	11
ParticipantIDs	doaj_primary_oai_doaj_org_article_7a124b05bca74977913eef2f5116d374 pubmedcentral_primary_oai_pubmedcentral_nih_gov_5715511 hal_primary_oai_HAL_inserm_02310904v1 proquest_miscellaneous_1973019890 gale_infotracmisc_A546364344 gale_infotracacademiconefile_A546364344 pubmed_primary_29202691 crossref_primary_10_1186_s12874_017_0434_1 crossref_citationtrail_10_1186_s12874_017_0434_1 springer_journals_10_1186_s12874_017_0434_1
PublicationCentury	2000
PublicationDate	2017-12-04
PublicationDateYYYYMMDD	2017-12-04
PublicationDate_xml	– month: 12 year: 2017 text: 2017-12-04 day: 04
PublicationDecade	2010
PublicationPlace	London
PublicationPlace_xml	– name: London – name: England
PublicationSubtitle	BMC series – open, inclusive and trusted
PublicationTitle	BMC medical research methodology
PublicationTitleAbbrev	BMC Med Res Methodol
PublicationTitleAlternate	BMC Med Res Methodol
PublicationYear	2017
Publisher	BioMed Central BioMed Central Ltd BMC
Publisher_xml	– name: BioMed Central – name: BioMed Central Ltd – name: BMC
References	SR Cole (434_CR11) 2003; 158 SR Cole (434_CR27) 2008; 168 H Lopez-Gatell (434_CR10) 2007; 165 M Guiguet (434_CR24) 2009; 10 G Vourli (434_CR15) 2015; 57 M Pang (434_CR39) 2016; 25 434_CR14 434_CR38 C Chao (434_CR40) 2012; 26 JM Robins (434_CR2) 1986; 7 Y Xiao (434_CR18) 2014; 109 LM Bodnar (434_CR12) 2004; 159 MA Hernan (434_CR1) 2004; 15 IB Tager (434_CR8) 2004; 15 H Bang (434_CR31) 2005; 61 JM Robins (434_CR5) 2000; 11 MA Hernan (434_CR6) 2001; 96 M van Der Laan (434_CR33) 2010; 6 D Westreich (434_CR16) 2012; 31 Y Xiao (434_CR17) 2010; 6 CJ Howe (434_CR9) 2012; 23 JG Young (434_CR19) 2010; 16 TJ VanderWeele (434_CR28) 2009; 20 JM Robins (434_CR32) 1994; 89 ME Karim (434_CR30) 2017; 36 434_CR21 M van Der Laan (434_CR34) 2010; 6 WG Havercroft (434_CR13) 2012; 31 JG Young (434_CR20) 2014; 33 JM Robins (434_CR3) 1999; 121 434_CR4 C Csajka (434_CR29) 2006; 33 RM Daniel (434_CR35) 2013; 32 DF McCaffrey (434_CR36) 2013; 32 C Piketty (434_CR26) 2012; 30 M Bruyand (434_CR23) 2015; 68 RA Ali (434_CR22) 2014; 20 M Mary-Krause (434_CR25) 2014; 43 AR Ellis (434_CR7) 2013; 66 PC Austin (434_CR37) 2013; 32
References_xml	– volume: 11 start-page: 550 issue: 5 year: 2000 ident: 434_CR5 publication-title: Epidemiology doi: 10.1097/00001648-200009000-00011 – volume: 31 start-page: 2098 issue: 19 year: 2012 ident: 434_CR16 publication-title: Stat Med doi: 10.1002/sim.5317 – volume: 57 start-page: 254 issue: 2 year: 2015 ident: 434_CR15 publication-title: Biom J doi: 10.1002/bimj.201300159 – volume: 66 start-page: S51 issue: 8 Suppl year: 2013 ident: 434_CR7 publication-title: J Clin Epidemiol doi: 10.1016/j.jclinepi.2013.03.020 – volume: 68 start-page: 568 issue: 5 year: 2015 ident: 434_CR23 publication-title: J Acquir Immune Defic Syndr doi: 10.1097/QAI.0000000000000523 – ident: 434_CR21 – volume: 20 start-page: 863 issue: 6 year: 2009 ident: 434_CR28 publication-title: Epidemiology doi: 10.1097/EDE.0b013e3181ba333c – volume: 96 start-page: 440 year: 2001 ident: 434_CR6 publication-title: J Am Stat Asso doi: 10.1198/016214501753168154 – volume: 121 start-page: 151 issue: 1 year: 1999 ident: 434_CR3 publication-title: Synthese doi: 10.1023/A:1005285815569 – volume: 32 start-page: 3388 issue: 19 year: 2013 ident: 434_CR36 publication-title: Stat Med doi: 10.1002/sim.5753 – volume: 30 start-page: 4360 issue: 35 year: 2012 ident: 434_CR26 publication-title: J Clin Oncol doi: 10.1200/JCO.2012.44.5486 – volume: 32 start-page: 1584 issue: 9 year: 2013 ident: 434_CR35 publication-title: Stat Med doi: 10.1002/sim.5686 – volume: 33 start-page: 227 issue: 3 year: 2006 ident: 434_CR29 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-005-9002-0 – volume: 6 start-page: 3 issue: 2 year: 2010 ident: 434_CR34 publication-title: Int J Biostat – volume: 25 start-page: 1925 issue: 5 year: 2016 ident: 434_CR39 publication-title: Stat Methods Med Res doi: 10.1177/0962280213505804 – volume: 165 start-page: 1134 issue: 10 year: 2007 ident: 434_CR10 publication-title: Am J Epidemiol doi: 10.1093/aje/kwk116 – ident: 434_CR14 doi: 10.1080/03610918.2016.1248574 – volume: 15 start-page: 479 issue: 4 year: 2004 ident: 434_CR8 publication-title: Epidemiology doi: 10.1097/01.ede.0000128401.55545.c6 – volume: 33 start-page: 1001 issue: 6 year: 2014 ident: 434_CR20 publication-title: Stat Med doi: 10.1002/sim.5994 – volume: 20 start-page: 106 issue: 1 year: 2014 ident: 434_CR22 publication-title: Lifetime Data Anal doi: 10.1007/s10985-013-9255-7 – volume: 26 start-page: 2223 issue: 17 year: 2012 ident: 434_CR40 publication-title: AIDS doi: 10.1097/QAD.0b013e32835935b3 – volume: 15 start-page: 615 year: 2004 ident: 434_CR1 publication-title: Epidemiology doi: 10.1097/01.ede.0000135174.63482.43 – volume: 109 start-page: 455 issue: 506 year: 2014 ident: 434_CR18 publication-title: J Am Stat Asso doi: 10.1080/01621459.2013.872650 – ident: 434_CR4 doi: 10.1201/9781420011579.ch23 – volume: 23 start-page: 574 issue: 4 year: 2012 ident: 434_CR9 publication-title: Epidemiology doi: 10.1097/EDE.0b013e31824d1ccb – volume: 36 start-page: 2032 issue: 13 year: 2017 ident: 434_CR30 publication-title: Stat Med doi: 10.1002/sim.7266 – volume: 6 start-page: 2 issue: 2 year: 2010 ident: 434_CR33 publication-title: Int J Biostat – volume: 158 start-page: 687 issue: 7 year: 2003 ident: 434_CR11 publication-title: Am J Epidemiol doi: 10.1093/aje/kwg206 – volume: 7 start-page: 1393 year: 1986 ident: 434_CR2 publication-title: Mathematical Modelling doi: 10.1016/0270-0255(86)90088-6 – volume: 61 start-page: 962 issue: 4 year: 2005 ident: 434_CR31 publication-title: Biometrics doi: 10.1111/j.1541-0420.2005.00377.x – volume: 6 issue: 2 year: 2010 ident: 434_CR17 publication-title: Int J Biostat doi: 10.2202/1557-4679.1208 – volume: 43 start-page: 1425 issue: 5 year: 2014 ident: 434_CR25 publication-title: Int J Epidemiol doi: 10.1093/ije/dyu002 – volume: 16 start-page: 71 issue: 1 year: 2010 ident: 434_CR19 publication-title: Lifetime Data Anal doi: 10.1007/s10985-009-9135-3 – volume: 168 start-page: 656 issue: 6 year: 2008 ident: 434_CR27 publication-title: Am J Epidemiol doi: 10.1093/aje/kwn164 – volume: 32 start-page: 2837 issue: 16 year: 2013 ident: 434_CR37 publication-title: Stat Med doi: 10.1002/sim.5705 – volume: 159 start-page: 926 issue: 10 year: 2004 ident: 434_CR12 publication-title: Am J Epidemiol doi: 10.1093/aje/kwh131 – volume: 10 start-page: 1152 issue: 12 year: 2009 ident: 434_CR24 publication-title: Lancet Oncol doi: 10.1016/S1470-2045(09)70282-7 – volume: 31 start-page: 4190 issue: 30 year: 2012 ident: 434_CR13 publication-title: Stat Med doi: 10.1002/sim.5472 – volume: 89 start-page: 846 issue: 427 year: 1994 ident: 434_CR32 publication-title: J Am Stat Assoc doi: 10.1080/01621459.1994.10476818 – ident: 434_CR38 doi: 10.1177/0962280216668554
SSID	ssj0017836
Score	2.2655137
Snippet	Background The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and... The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and applied to... BACKGROUND:The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival analysis and... Abstract Background The Marginal Structural Cox Model (Cox-MSM), an alternative approach to handle time-dependent confounder, was introduced for survival...
SourceID	doaj pubmedcentral hal proquest gale pubmed crossref springer
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	160
SubjectTerms	Algorithms Analysis Anus Neoplasms - chemically induced Anus Neoplasms - epidemiology Causal inference CD4 Lymphocyte Count Data analysis Female Health Sciences Highly active antiretroviral therapy HIV Infections - drug therapy HIV Infections - epidemiology HIV Infections - immunology HIV Protease Inhibitors - adverse effects HIV Protease Inhibitors - therapeutic use Humans Life Sciences Longitudinal data Male Marginal structural models Medicine Medicine & Public Health Multitherapy Pharmaceutical sciences Proportional Hazards Models Protease inhibitors Proteases Research Article Santé publique et épidémiologie Statistical Theory and Methods statistics and modelling Statistics for Life Sciences Theory of Medicine/Bioethics Time-dependent confounding Treatment Outcome
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELagQogL4s2WgowEQgJFdeLEj-OCqHooVQ8g9WY5frSL2AR1S9UjP50ZxwmECrhwTcaO4xmPv5HH3xDyovWctRHWd5SlL2oum0JHFYuqCbKtbClFTJo-kIeH6vhYH_1S6gtzwgZ64GHidqWFHahlTeusrAGs6JKHEKsIQEF4LhMTKKCeMZjK5wd4NyGfYZZK7G5KpHUv0COzmtdFOduFEln_5JKvn2JG5FW4eTVr8rej07Qj7d0htzOUpMvhF-6Sa6G7R25-yIfl98n3o5-XAmgfKUA9urZnqQ4WHXhjkXODuv6SpoI4FKQpsm4giu1O6Gq6rEVt5-nnHrr1NGeApB7HLPUNPUGvCS2gtzUE20nfD8invfcf3-0XueBC4QDonBdaOR4lt1Y1DYdIJgCeAx8gZJSSici90MEFV7bCsxA0SDDPNGuV9bXVIPCQbHV9Fx4TClGiaLlzXMF8M28hqlNtrStrRVV5rxeEjQowLrORY1GMLyZFJUqYQWcGdGZQZ6ZckNdTk68DFcffhN-iVidBZNFOD8C2TLYt8y_bWpBXaBMG1zoMztl8ZQF-EVmzzBJrCQCkq0FyZyYJa9TNXr8Eq5oNZn95ANLgZtYGKfhgGusLGPXz0e4MdoEJcF3ov21MqdEVa6XZgjwa7HDqDguOVUJDazmz0Nn35m-61WmiEm8kQmZo-Wa0ZZN92ObPc7v9P-b2CblV4XrEzKB6h2yB1Yen5Ia7OF9tzp6l1fwD0LhLjg priority: 102 providerName: Directory of Open Access Journals
Title	Performance of the marginal structural cox model for estimating individual and joined effects of treatments given in combination
URI	https://link.springer.com/article/10.1186/s12874-017-0434-1 https://www.ncbi.nlm.nih.gov/pubmed/29202691 https://www.proquest.com/docview/1973019890 https://inserm.hal.science/inserm-02310904 https://pubmed.ncbi.nlm.nih.gov/PMC5715511 https://doaj.org/article/7a124b05bca74977913eef2f5116d374
Volume	17
WOSCitedRecordID	wos000417509000002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVADU databaseName: BioMed Central customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: RBZ dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: Springer LINK customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: RSV dateStart: 20011201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Zb9NAEF7RFiFeuI9AiRYJhASy6nOPxxS1KlIbReVQeFqt90iDiI3iUvHIT2dmYxuZAhK8REo8u16PZ2ZnMrPfEPKstFlcetBvzxMb5RkvIumFj9LC8TLVCWc-vOljPp2K-VzO2nPcTVft3qUkg6UOai3YXpMgNHuEVjXOszyCkGcHdjuB2nj69kOfOsBjCW368rfDBhtQwOnvrfHWGRZDXvY0LxdM_pI1DZvR4c3_eoxb5Ebre9LJRlhukyuuukOunbTZ9bvk--znKQJaewq-IV3pdWicRTdAswjSQU39jYYOOhSoKcJ0oNtbLeiyP91FdWXppxqmtbQtGQkzdmXtDV2gmYURMNsKovMgIPfI-8ODd6-PorZDQ2TAMzqPpDCZ55nWoigyCH0cOIBgNBj3nMfMZ5ZJZ5xJSmZj5yRQxDaWcSm0zbUEgvtku6or95BQCCtZmRmTCeBKbDWEgaLMZao1S1Nr5YjE3WtTpoUvxy4an1UIYwRTG84q4KxCzqpkRF72Q75ssDv-RryPstATIux2-KFeL1SrxYprcIfKuCiN5jl4zjLJnPOpB6-V2YznI_ICJUmhcYDFGd2ecYBHRJgtNcHmA-AD5kC5O6AEpTaDy89BFgeLOZocAzXYpZVCzD5gY34Bq37aSavCKbBirnL110YlEm23FDIekQcb6e2nww5lKZMwmg_kenC_4ZVqeRawxwuOPjaMfNVJt2qNXvNn3j76J-rH5HqK6oE1Q_ku2Qbxdk_IVXNxvmzWY7LF5zx8ijHZ2T-Yzk7H4Y8U-DZ7czL7OA7m4Ac9DlXZ
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwELagIODC-7FQwEggJFBEEjt2fFwQVRHbVQUF9WY5fmwXsQnalIojP50ZbxIUCkhw3Yy9zmRm_I08_oaQx5VjaRXAv4PMXMKZLBIVypDkhZdVbjIpQvzSMzmfl4eHar-7x9321e79kWSM1NGtS_GizZCaPcGomnLGE0h5znHYsLCO7937j8PRAV5L6I4vfztstAFFnv4hGp89wmLI00jzdMHkL6emcTPaufJfr3GVXO6wJ51ujOUaOePr6-TCXne6foN83_95i4A2gQI2pCuzjo2z6IZoFkk6qG2-0dhBh4I0RZoOhL31gi6H213U1I5-amBaR7uSkThjX9be0gWGWRgBs60gO48GcpN82Hl98Go36To0JBaQ0XGiSsuCZMaURcEg9fEAACFoCBmkTEVgTihvvc0q4VLvFUikLlVpVRrHjQKBW2Srbmp_h1BIK0XFrGUlaCV1BtLAsuIqN0bkuXNqQtL-s2nb0ZdjF43POqYxpdAbzWrQrEbN6mxCng1Dvmy4O_4m_BJtYRBE2u34Q7Ne6M6LtTQAh6q0qKyRHJCzypj3IQ-AWoVjkk_IU7QkjcEBFmdNd8cBXhFptvQUmw8ABuQguT2SBKe2o8dPwBZHi9mdzkAa4tJKI2cfqJGfwKof9daqcQqsmKt987XVmcLYrUqVTsjtjfUO02GHslwoGC1Hdj36v_GTenkUuccLiRgbRj7vrVt3Qa_9s27v_pP0Q3Jx92Bvpmdv5m_vkUs5ugrWD_FtsgWm7u-T8_bkeNmuH0TH_wEFnFKt
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwELagoIoX7mOhgJFASKCozmXHj8tRFbGsVuJQ3yzHx3YRm1SbpeKRn86Mc6BQQEK8JjOOMxlPvpHH3xDyuLQpKz2sby9iG2WpyCPpCx8luRNlomPBffjSMzGfF0dHctH1OW36avd-S7I904AsTdV2_8T6dokXfL-JkaY9wgjLsjSLIP25kGHPIEzX338athHwiEK3lflbtdHPKHD2D5H5_DEWRp5FnWeLJ3_ZQQ0_poMr__1KV8nlDpPSaetE18g5V10nu--6Xfcb5Pvi5-kCWnsKmJGu9SY01KItAS2Sd1BTf6Ohsw4FaYr0HQiHqyVdDae-qK4s_VzDsJZ2pSRhxL7cvaFLDL-gAaOtIWsPjnOTfDx4_eHlYdR1bogMIKZtJAuTepFqXeR5CimRA2AIwYQLLwTjPrVcOuNMXHLLnJMgwSyTrCy0zbQEgVtkp6ord4dQSDd5mRqTFmAVZjWkh0WZyURrniTWyglh_SdUpqM1x-4aX1RIbwquWssqsKxCy6p4Qp4NKictp8ffhF-gXwyCSMcdLtSbpepWtxIaYFLJ8tJokQGilnHqnE88oFluU5FNyFP0KoVBAyZndHf2AV4R6bfUFJsSADbMQHJvJAmL3YxuPwG_HE3mcDoDaYhXa4VcfmDG7BRm_aj3XIVDYCVd5eqvjYolxnRZSDYht1tPHobDzmUJl6AtRj4-et74TrU6DpzkuUDsDZrPe09XXTBs_mzbu_8k_ZDsLl4dqNmb-dt75FKCKwXLirI9sgOe7u6Ti-Z0u2o2D0IM-AEZ71uR
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Performance+of+the+marginal+structural+cox+model+for+estimating+individual+and+joined+effects+of+treatments+given+in+combination&rft.jtitle=BMC+medical+research+methodology&rft.au=Lusivika-Nzinga%2C+Clovis&rft.au=Selinger-Leneman%2C+Hana&rft.au=Grabar%2C+Sophie&rft.au=Costagliola%2C+Dominique&rft.date=2017-12-04&rft.pub=BioMed+Central&rft.eissn=1471-2288&rft.volume=17&rft_id=info:doi/10.1186%2Fs12874-017-0434-1&rft_id=info%3Apmid%2F29202691&rft.externalDocID=PMC5715511
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2288&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2288&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2288&client=summon