Adjusted restricted mean survival times in observational studies
In observational studies with censored data, exposure‐outcome associations are commonly measured with adjusted hazard ratios from multivariable Cox proportional hazards models. The difference in restricted mean survival times (RMSTs) up to a pre‐specified time point is an alternative measure that of...
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| Vydané v: | Statistics in medicine Ročník 38; číslo 20; s. 3832 - 3860 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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England
Wiley Subscription Services, Inc
10.09.2019
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| ISSN: | 0277-6715, 1097-0258, 1097-0258 |
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| Abstract | In observational studies with censored data, exposure‐outcome associations are commonly measured with adjusted hazard ratios from multivariable Cox proportional hazards models. The difference in restricted mean survival times (RMSTs) up to a pre‐specified time point is an alternative measure that offers a clinically meaningful interpretation. Several regression‐based methods exist to estimate an adjusted difference in RMSTs, but they digress from the model‐free method of taking the area under the survival function. We derive the adjusted RMST by integrating an adjusted Kaplan‐Meier estimator with inverse probability weighting (IPW). The adjusted difference in RMSTs is the area between the two IPW‐adjusted survival functions. In a Monte Carlo‐type simulation study, we demonstrate that the proposed estimator performs as well as two regression‐based approaches: the ANCOVA‐type method of Tian et al and the pseudo‐observation method of Andersen et al. We illustrate the methods by reexamining the association between total cholesterol and the 10‐year risk of coronary heart disease in the Framingham Heart Study. |
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| AbstractList | In observational studies with censored data, exposure‐outcome associations are commonly measured with adjusted hazard ratios from multivariable Cox proportional hazards models. The difference in restricted mean survival times (RMSTs) up to a pre‐specified time point is an alternative measure that offers a clinically meaningful interpretation. Several regression‐based methods exist to estimate an adjusted difference in RMSTs, but they digress from the model‐free method of taking the area under the survival function. We derive the adjusted RMST by integrating an adjusted Kaplan‐Meier estimator with inverse probability weighting (IPW). The adjusted difference in RMSTs is the area between the two IPW‐adjusted survival functions. In a Monte Carlo‐type simulation study, we demonstrate that the proposed estimator performs as well as two regression‐based approaches: the ANCOVA‐type method of Tian et al and the pseudo‐observation method of Andersen et al. We illustrate the methods by reexamining the association between total cholesterol and the 10‐year risk of coronary heart disease in the Framingham Heart Study. In observational studies with censored data, exposure-outcome associations are commonly measured with adjusted hazard ratios from multivariable Cox proportional hazards models. The difference in restricted mean survival times (RMSTs) up to a pre-specified time point is an alternative measure that offers a clinically meaningful interpretation. Several regression-based methods exist to estimate an adjusted difference in RMSTs, but they digress from the model-free method of taking the area under the survival function. We derive the adjusted RMST by integrating an adjusted Kaplan-Meier estimator with inverse probability weighting (IPW). The adjusted difference in RMSTs is the area between the two IPW-adjusted survival functions. In a Monte Carlo-type simulation study, we demonstrate that the proposed estimator performs as well as two regression-based approaches: the ANCOVA-type method of Tian et al and the pseudo-observation method of Andersen et al. We illustrate the methods by reexamining the association between total cholesterol and the 10-year risk of coronary heart disease in the Framingham Heart Study.In observational studies with censored data, exposure-outcome associations are commonly measured with adjusted hazard ratios from multivariable Cox proportional hazards models. The difference in restricted mean survival times (RMSTs) up to a pre-specified time point is an alternative measure that offers a clinically meaningful interpretation. Several regression-based methods exist to estimate an adjusted difference in RMSTs, but they digress from the model-free method of taking the area under the survival function. We derive the adjusted RMST by integrating an adjusted Kaplan-Meier estimator with inverse probability weighting (IPW). The adjusted difference in RMSTs is the area between the two IPW-adjusted survival functions. In a Monte Carlo-type simulation study, we demonstrate that the proposed estimator performs as well as two regression-based approaches: the ANCOVA-type method of Tian et al and the pseudo-observation method of Andersen et al. We illustrate the methods by reexamining the association between total cholesterol and the 10-year risk of coronary heart disease in the Framingham Heart Study. In observational studies with censored data, exposure-outcome associations are commonly measured with adjusted hazard ratios (HRs) from multivariable Cox proportional hazards models. The difference in restricted mean survival times (RMST) up to a pre-specified time point is an alternative measure that offers a clinically meaningful interpretation. Several regression-based methods exist to estimate an adjusted difference in RMSTs, but they digress from the model-free method of taking the area under the survival function. We derive the adjusted RMST by integrating an adjusted Kaplan-Meier estimator with inverse probability weighting (IPW). The adjusted difference in RMSTs is the area between the two IPW-adjusted survival functions. In a Monte Carlo-type simulation study, we demonstrate that the proposed estimator performs as well as two regression-based approaches: the ANCOVA-type method of Tian et al1 and the pseudo-observation method of Andersen et al.2–4 We illustrate the methods by re-examining the association between total cholesterol and the 10-year risk of coronary heart disease in the Framingham Heart Study. |
| Author | LaValley, Michael P. Galea, Sandro Sullivan, Lisa M. Conner, Sarah C. Benjamin, Emelia J. Trinquart, Ludovic |
| AuthorAffiliation | 1 Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA 2 National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, MA 3 Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA 4 Section of Cardiovascular Medicine, Evans Department of Medicine, Boston University School of Medicine, Boston, MA |
| AuthorAffiliation_xml | – name: 1 Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA – name: 2 National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, MA – name: 4 Section of Cardiovascular Medicine, Evans Department of Medicine, Boston University School of Medicine, Boston, MA – name: 3 Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA |
| Author_xml | – sequence: 1 givenname: Sarah C. orcidid: 0000-0002-0929-9948 surname: Conner fullname: Conner, Sarah C. email: sconner@bu.edu organization: National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study – sequence: 2 givenname: Lisa M. surname: Sullivan fullname: Sullivan, Lisa M. organization: Boston University – sequence: 3 givenname: Emelia J. surname: Benjamin fullname: Benjamin, Emelia J. organization: Boston University – sequence: 4 givenname: Michael P. surname: LaValley fullname: LaValley, Michael P. organization: Boston University – sequence: 5 givenname: Sandro surname: Galea fullname: Galea, Sandro organization: Boston University – sequence: 6 givenname: Ludovic surname: Trinquart fullname: Trinquart, Ludovic organization: National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31119770$$D View this record in MEDLINE/PubMed |
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| Keywords | inverse probability weighting survival analysis observational studies restricted mean survival time time-to-event data propensity score |
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| SubjectTerms | Analysis of Variance Cardiovascular disease Computer Simulation Health risk assessment Humans inverse probability weighting Kaplan-Meier Estimate Monte Carlo Method Observational studies Observational Studies as Topic - methods propensity score restricted mean survival time Survival Analysis time‐to‐event data |
| Title | Adjusted restricted mean survival times in observational studies |
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