Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis

Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be inco...
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Autor*in:	Sitlani, Colleen M. [verfasserIn] Lumley, Thomas [verfasserIn] McKnight, Barbara [verfasserIn] Rice, Kenneth M. [verfasserIn] Olson, Nels C. [verfasserIn] Doyle, Margaret F. [verfasserIn] Huber, Sally A. [verfasserIn] Tracy, Russell P. [verfasserIn] Psaty, Bruce M. [verfasserIn] Delaney, Joseph A. C. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Cox regression Sampling weights Case-cohort design Robust regression Immune cell traits

Übergeordnetes Werk:	Enthalten in: BMC medical research methodology - London : BioMed Central, 2001, 20(2020), 1 vom: 14. März
Übergeordnetes Werk:	volume:20 ; year:2020 ; number:1 ; day:14 ; month:03

Links:	Volltext

DOI / URN:	10.1186/s12874-020-00945-9

Katalog-ID:	SPR039096858

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520			\|a Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers.
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allfields	10.1186/s12874-020-00945-9 doi (DE-627)SPR039096858 (SPR)s12874-020-00945-9-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Sitlani, Colleen M. verfasserin aut Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers. Cox regression (dpeaa)DE-He213 Sampling weights (dpeaa)DE-He213 Case-cohort design (dpeaa)DE-He213 Robust regression (dpeaa)DE-He213 Immune cell traits (dpeaa)DE-He213 Lumley, Thomas verfasserin aut McKnight, Barbara verfasserin aut Rice, Kenneth M. verfasserin aut Olson, Nels C. verfasserin aut Doyle, Margaret F. verfasserin aut Huber, Sally A. verfasserin aut Tracy, Russell P. verfasserin aut Psaty, Bruce M. verfasserin aut Delaney, Joseph A. C. verfasserin aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 20(2020), 1 vom: 14. März (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:20 year:2020 number:1 day:14 month:03 https://dx.doi.org/10.1186/s12874-020-00945-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 14 03
spelling	10.1186/s12874-020-00945-9 doi (DE-627)SPR039096858 (SPR)s12874-020-00945-9-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Sitlani, Colleen M. verfasserin aut Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers. Cox regression (dpeaa)DE-He213 Sampling weights (dpeaa)DE-He213 Case-cohort design (dpeaa)DE-He213 Robust regression (dpeaa)DE-He213 Immune cell traits (dpeaa)DE-He213 Lumley, Thomas verfasserin aut McKnight, Barbara verfasserin aut Rice, Kenneth M. verfasserin aut Olson, Nels C. verfasserin aut Doyle, Margaret F. verfasserin aut Huber, Sally A. verfasserin aut Tracy, Russell P. verfasserin aut Psaty, Bruce M. verfasserin aut Delaney, Joseph A. C. verfasserin aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 20(2020), 1 vom: 14. März (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:20 year:2020 number:1 day:14 month:03 https://dx.doi.org/10.1186/s12874-020-00945-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 14 03
allfields_unstemmed	10.1186/s12874-020-00945-9 doi (DE-627)SPR039096858 (SPR)s12874-020-00945-9-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Sitlani, Colleen M. verfasserin aut Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers. Cox regression (dpeaa)DE-He213 Sampling weights (dpeaa)DE-He213 Case-cohort design (dpeaa)DE-He213 Robust regression (dpeaa)DE-He213 Immune cell traits (dpeaa)DE-He213 Lumley, Thomas verfasserin aut McKnight, Barbara verfasserin aut Rice, Kenneth M. verfasserin aut Olson, Nels C. verfasserin aut Doyle, Margaret F. verfasserin aut Huber, Sally A. verfasserin aut Tracy, Russell P. verfasserin aut Psaty, Bruce M. verfasserin aut Delaney, Joseph A. C. verfasserin aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 20(2020), 1 vom: 14. März (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:20 year:2020 number:1 day:14 month:03 https://dx.doi.org/10.1186/s12874-020-00945-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 14 03
allfieldsGer	10.1186/s12874-020-00945-9 doi (DE-627)SPR039096858 (SPR)s12874-020-00945-9-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Sitlani, Colleen M. verfasserin aut Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers. Cox regression (dpeaa)DE-He213 Sampling weights (dpeaa)DE-He213 Case-cohort design (dpeaa)DE-He213 Robust regression (dpeaa)DE-He213 Immune cell traits (dpeaa)DE-He213 Lumley, Thomas verfasserin aut McKnight, Barbara verfasserin aut Rice, Kenneth M. verfasserin aut Olson, Nels C. verfasserin aut Doyle, Margaret F. verfasserin aut Huber, Sally A. verfasserin aut Tracy, Russell P. verfasserin aut Psaty, Bruce M. verfasserin aut Delaney, Joseph A. C. verfasserin aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 20(2020), 1 vom: 14. März (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:20 year:2020 number:1 day:14 month:03 https://dx.doi.org/10.1186/s12874-020-00945-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 14 03
allfieldsSound	10.1186/s12874-020-00945-9 doi (DE-627)SPR039096858 (SPR)s12874-020-00945-9-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Sitlani, Colleen M. verfasserin aut Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers. Cox regression (dpeaa)DE-He213 Sampling weights (dpeaa)DE-He213 Case-cohort design (dpeaa)DE-He213 Robust regression (dpeaa)DE-He213 Immune cell traits (dpeaa)DE-He213 Lumley, Thomas verfasserin aut McKnight, Barbara verfasserin aut Rice, Kenneth M. verfasserin aut Olson, Nels C. verfasserin aut Doyle, Margaret F. verfasserin aut Huber, Sally A. verfasserin aut Tracy, Russell P. verfasserin aut Psaty, Bruce M. verfasserin aut Delaney, Joseph A. C. verfasserin aut Enthalten in BMC medical research methodology London : BioMed Central, 2001 20(2020), 1 vom: 14. März (DE-627)326643818 (DE-600)2041362-2 1471-2288 nnns volume:20 year:2020 number:1 day:14 month:03 https://dx.doi.org/10.1186/s12874-020-00945-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.00 ASE AR 20 2020 1 14 03
language	English
source	Enthalten in BMC medical research methodology 20(2020), 1 vom: 14. März volume:20 year:2020 number:1 day:14 month:03
sourceStr	Enthalten in BMC medical research methodology 20(2020), 1 vom: 14. März volume:20 year:2020 number:1 day:14 month:03
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cox regression Sampling weights Case-cohort design Robust regression Immune cell traits
dewey-raw	610
isfreeaccess_bool	true
container_title	BMC medical research methodology
authorswithroles_txt_mv	Sitlani, Colleen M. @@aut@@ Lumley, Thomas @@aut@@ McKnight, Barbara @@aut@@ Rice, Kenneth M. @@aut@@ Olson, Nels C. @@aut@@ Doyle, Margaret F. @@aut@@ Huber, Sally A. @@aut@@ Tracy, Russell P. @@aut@@ Psaty, Bruce M. @@aut@@ Delaney, Joseph A. C. @@aut@@
publishDateDaySort_date	2020-03-14T00:00:00Z
hierarchy_top_id	326643818
dewey-sort	3610
id	SPR039096858
language_de	englisch
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When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. 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author	Sitlani, Colleen M.
spellingShingle	Sitlani, Colleen M. ddc 610 bkl 44.00 misc Cox regression misc Sampling weights misc Case-cohort design misc Robust regression misc Immune cell traits Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis
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topic_title	610 ASE 44.00 bkl Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis Cox regression (dpeaa)DE-He213 Sampling weights (dpeaa)DE-He213 Case-cohort design (dpeaa)DE-He213 Robust regression (dpeaa)DE-He213 Immune cell traits (dpeaa)DE-He213
topic	ddc 610 bkl 44.00 misc Cox regression misc Sampling weights misc Case-cohort design misc Robust regression misc Immune cell traits
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title	Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis
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title_full	Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis
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author_browse	Sitlani, Colleen M. Lumley, Thomas McKnight, Barbara Rice, Kenneth M. Olson, Nels C. Doyle, Margaret F. Huber, Sally A. Tracy, Russell P. Psaty, Bruce M. Delaney, Joseph A. C.
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title_sort	incorporating sampling weights into robust estimation of cox proportional hazards regression model, with illustration in the multi-ethnic study of atherosclerosis
title_auth	Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis
abstract	Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers.
abstractGer	Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers.
abstract_unstemmed	Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers.
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title_short	Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis
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Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis

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