Sensitivity analysis of treatment effect to unmeasured confounding in observational studies with survival and competing risks outcomes

No unmeasured confounding is often assumed in estimating treatment effects in observational data, whether using classical regression models or approaches such as propensity scores and inverse probability weighting. However, in many such studies collection of confounders cannot possibly be exhaustive...

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Veröffentlicht in:	Statistics in medicine Jg. 39; H. 24; S. 3397 - 3411
Hauptverfasser:	Huang, Rong, Xu, Ronghui, Dulai, Parambir S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Wiley Subscription Services, Inc 30.10.2020
Schlagworte:	Algorithms causal inference Causality Confounding Factors, Epidemiologic Cox model expectation‐maximization algorithm Humans inverse probability weighting Observational studies Propensity Score proportional hazards regression regression adjustment Sensitivity analysis simulated confounder stochastic EM proportional hazards regression stochastic EM Cox model expectation-maximization algorithm simulated confounder inverse probability weighting causal inference regression adjustment
ISSN:	0277-6715, 1097-0258, 1097-0258
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Abstract	No unmeasured confounding is often assumed in estimating treatment effects in observational data, whether using classical regression models or approaches such as propensity scores and inverse probability weighting. However, in many such studies collection of confounders cannot possibly be exhaustive in practice, and it is crucial to examine the extent to which the resulting estimate is sensitive to the unmeasured confounders. We consider this problem for survival and competing risks data. Due to the complexity of models for such data, we adapt the simulated potential confounder approach of Carnegie et al (2016), which provides a general tool for sensitivity analysis due to unmeasured confounding. More specifically, we specify one sensitivity parameter to quantify the association between an unmeasured confounder and the exposure or treatment received, and another set of parameters to quantify the association between the confounder and the time‐to‐event outcomes. By varying the magnitudes of the sensitivity parameters, we estimate the treatment effect of interest using the stochastic expectation‐maximization (EM) and the EM algorithms. We demonstrate the performance of our methods on simulated data, and apply them to a comparative effectiveness study in inflammatory bowel disease. An R package “survSens” is available on CRAN that implements the proposed methodology.
AbstractList	No unmeasured confounding is often assumed in estimating treatment effects in observational data, whether using classical regression models or approaches such as propensity scores and inverse probability weighting. However, in many such studies collection of confounders cannot possibly be exhaustive in practice, and it is crucial to examine the extent to which the resulting estimate is sensitive to the unmeasured confounders. We consider this problem for survival and competing risks data. Due to the complexity of models for such data, we adapt the simulated potential confounder approach of Carnegie et al (2016), which provides a general tool for sensitivity analysis due to unmeasured confounding. More specifically, we specify one sensitivity parameter to quantify the association between an unmeasured confounder and the exposure or treatment received, and another set of parameters to quantify the association between the confounder and the time-to-event outcomes. By varying the magnitudes of the sensitivity parameters, we estimate the treatment effect of interest using the stochastic expectation-maximization (EM) and the EM algorithms. We demonstrate the performance of our methods on simulated data, and apply them to a comparative effectiveness study in inflammatory bowel disease. An R package "survSens" is available on CRAN that implements the proposed methodology. No unmeasured confounding is often assumed in estimating treatment effects in observational data, whether using classical regression models or approaches such as propensity scores and inverse probability weighting. However, in many such studies collection of confounders cannot possibly be exhaustive in practice, and it is crucial to examine the extent to which the resulting estimate is sensitive to the unmeasured confounders. We consider this problem for survival and competing risks data. Due to the complexity of models for such data, we adapt the simulated potential confounder approach of Carnegie et al (2016), which provides a general tool for sensitivity analysis due to unmeasured confounding. More specifically, we specify one sensitivity parameter to quantify the association between an unmeasured confounder and the exposure or treatment received, and another set of parameters to quantify the association between the confounder and the time‐to‐event outcomes. By varying the magnitudes of the sensitivity parameters, we estimate the treatment effect of interest using the stochastic expectation‐maximization (EM) and the EM algorithms. We demonstrate the performance of our methods on simulated data, and apply them to a comparative effectiveness study in inflammatory bowel disease. An R package “survSens” is available on CRAN that implements the proposed methodology. No unmeasured confounding is often assumed in estimating treatment effects in observational data, whether using classical regression models or approaches such as propensity scores and inverse probability weighting. However, in many such studies collection of confounders cannot possibly be exhaustive in practice, and it is crucial to examine the extent to which the resulting estimate is sensitive to the unmeasured confounders. We consider this problem for survival and competing risks data. Due to the complexity of models for such data, we adapt the simulated potential confounder approach of Carnegie et al (2016), which provides a general tool for sensitivity analysis due to unmeasured confounding. More specifically, we specify one sensitivity parameter to quantify the association between an unmeasured confounder and the exposure or treatment received, and another set of parameters to quantify the association between the confounder and the time-to-event outcomes. By varying the magnitudes of the sensitivity parameters, we estimate the treatment effect of interest using the stochastic expectation-maximization (EM) and the EM algorithms. We demonstrate the performance of our methods on simulated data, and apply them to a comparative effectiveness study in inflammatory bowel disease. An R package "survSens" is available on CRAN that implements the proposed methodology.No unmeasured confounding is often assumed in estimating treatment effects in observational data, whether using classical regression models or approaches such as propensity scores and inverse probability weighting. However, in many such studies collection of confounders cannot possibly be exhaustive in practice, and it is crucial to examine the extent to which the resulting estimate is sensitive to the unmeasured confounders. We consider this problem for survival and competing risks data. Due to the complexity of models for such data, we adapt the simulated potential confounder approach of Carnegie et al (2016), which provides a general tool for sensitivity analysis due to unmeasured confounding. More specifically, we specify one sensitivity parameter to quantify the association between an unmeasured confounder and the exposure or treatment received, and another set of parameters to quantify the association between the confounder and the time-to-event outcomes. By varying the magnitudes of the sensitivity parameters, we estimate the treatment effect of interest using the stochastic expectation-maximization (EM) and the EM algorithms. We demonstrate the performance of our methods on simulated data, and apply them to a comparative effectiveness study in inflammatory bowel disease. An R package "survSens" is available on CRAN that implements the proposed methodology.
Author	Dulai, Parambir S. Huang, Rong Xu, Ronghui
Author_xml	– sequence: 1 givenname: Rong surname: Huang fullname: Huang, Rong organization: University of California San Diego – sequence: 2 givenname: Ronghui orcidid: 0000-0002-2822-0561 surname: Xu fullname: Xu, Ronghui email: rxu@health.ucsd.edu organization: University of California San Diego – sequence: 3 givenname: Parambir S. surname: Dulai fullname: Dulai, Parambir S. organization: University of California San Diego
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References_xml	– year: 2011 – volume: 39 start-page: 1 issue: 1 year: 1977 end-page: 22 article-title: Maximum likelihood from incomplete data via the EM algorithm publication-title: J R Stat Soc Series B (Methodological) – volume: 154 start-page: S369 issue: 6 year: 2019 end-page: S370 article-title: Comparative effectiveness of vedolizumab and tumor necrosis factor‐antagonist therapy in Crohn's disease: a multicenter consortium propensity score‐matched analysis publication-title: Gastroenterology – volume: 9 start-page: 403 issue: 4 year: 2004 article-title: Propensity score estimation with boosted regression for evaluating causal effects in observational studies publication-title: Psychol Methods – volume: 111 start-page: 1147 issue: 8 year: 2016 article-title: The real‐world effectiveness and safety of vedolizumab for moderate–severe Crohn's disease: results from the US VICTORY consortium publication-title: Am J Gastroenterol – volume: 174 start-page: 345 issue: 3 year: 2011 end-page: 353 article-title: Propensity score‐based sensitivity analysis method for uncontrolled confounding publication-title: Am J Epidemiol – volume: 34 start-page: 3661 year: 2015 end-page: 3679 article-title: Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies publication-title: Stat Med – volume: 34 start-page: 187 issue: 2 year: 1972 end-page: 202 article-title: Regression models and life‐tables publication-title: J R Stat Soc Series B (Methodological) – volume: 54 start-page: 948 year: 1998 end-page: 963 article-title: Assessing the sensitivity of regression results to unmeasured confounders in observational studies publication-title: Biometrics – volume: 6 start-page: 457 issue: 3 year: 2000 end-page: 489 article-title: The stochastic EM algorithm: estimation and asymptotic results publication-title: Bernoulli – volume: 28 start-page: 956 issue: 6 year: 2009 end-page: 971 article-title: Simulating competing risks data in survival analysis publication-title: Stat Med – volume: 29 start-page: 596 issue: 4 year: 2014 article-title: Nonparametric bounds and sensitivity analysis of treatment effects publication-title: Stat Sci – year: 2002 – volume: 9 start-page: 395 issue: 3 year: 2016 end-page: 420 article-title: Assessing sensitivity to unmeasured confounding using a simulated potential confounder publication-title: J Res Educ Effect – volume: 14 start-page: 570 issue: 6 year: 2013 end-page: 580 article-title: An introduction to sensitivity analysis for unobserved confounding in nonexperimental prevention research publication-title: Prev Sci – volume: 113 start-page: 1345 issue: 9 year: 2018 article-title: Vedolizumab for ulcerative colitis: treatment outcomes from the VICTORY Consortium publication-title: Am J Gastroenterol – volume: 53 start-page: 822 issue: 5 year: 2011 end-page: 837 article-title: Sensitivity analysis for causal inference using inverse probability weighting publication-title: Biom J – volume: 86 start-page: 62 year: 2019 end-page: 67 article-title: Statistical sensitivity analysis for the estimation of fetal alcohol spectrum disorders prevalence publication-title: Reprod Toxicol – volume: 154 start-page: S68 issue: 6 year: 2019 article-title: Comparative safety profile of vedolizumab and tumor necrosis factor‐antagonist therapy for inflammatory bowel disease: a multicenter consortium propensity score‐matched analysis publication-title: Gastroenterology – volume: 44 start-page: 226 issue: 2 year: 1982 end-page: 233 article-title: Finding the observed information matrix when using the EM algorithm publication-title: J R Stat Soc Series B (Methodological) – volume: 19 start-page: 3309 issue: 24 year: 2000 end-page: 3324 article-title: Proportional hazards model with random effects publication-title: Stat Med – ident: e_1_2_7_5_1 doi: 10.1093/aje/kwr096 – ident: e_1_2_7_13_1 doi: 10.1080/19345747.2015.1078862 – ident: e_1_2_7_2_1 doi: 10.1007/978-1-4757-3692-2 – ident: e_1_2_7_7_1 doi: 10.1002/sim.6607 – ident: e_1_2_7_6_1 doi: 10.1002/bimj.201100042 – volume-title: The Statistical Analysis of Failure Time Data year: 2011 ident: e_1_2_7_15_1 – ident: e_1_2_7_16_1 doi: 10.1111/j.2517-6161.1977.tb01600.x – ident: e_1_2_7_23_1 doi: 10.1214/14-STS499 – ident: e_1_2_7_19_1 doi: 10.2307/3318671 – ident: e_1_2_7_10_1 doi: 10.1038/ajg.2016.236 – ident: e_1_2_7_17_1 doi: 10.1002/1097-0258(20001230)19:24<3309::AID-SIM825>3.0.CO;2-9 – ident: e_1_2_7_9_1 doi: 10.1038/s41395-018-0162-0 – ident: e_1_2_7_14_1 doi: 10.1111/j.2517-6161.1972.tb00899.x – ident: e_1_2_7_20_1 doi: 10.1002/sim.3516 – ident: e_1_2_7_4_1 doi: 10.2307/2533848 – ident: e_1_2_7_18_1 doi: 10.1111/j.2517-6161.1982.tb01203.x – ident: e_1_2_7_22_1 doi: 10.1016/j.reprotox.2019.04.002 – volume-title: The Facts About Inflammatory Bowel Diseases year: 2011 ident: e_1_2_7_8_1 – ident: e_1_2_7_3_1 doi: 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SubjectTerms	Algorithms causal inference Causality Confounding Factors, Epidemiologic Cox model expectation‐maximization algorithm Humans inverse probability weighting Observational studies Propensity Score proportional hazards regression regression adjustment Sensitivity analysis simulated confounder stochastic EM
Title	Sensitivity analysis of treatment effect to unmeasured confounding in observational studies with survival and competing risks outcomes
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