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2019-04-29T09:54:50.121-04:00
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Changbin Guo and Yu Liang
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<p>Survival analysis handles time-to-event data. Classical methods, such as the log-rank test and the Cox proportional hazards model, focus on the hazard function and are most suitable when the proportional hazards assumption holds. When it does not hold, restricted mean survival time (RMST) methods often apply. The RMST is the expected survival time subject to a specific time horizon, and it is an alternative measure to summarize the survival profile. RMST-based inference has attracted attention from practitioners for its capability to handle nonproportionality. This paper introduces RMST methods in SAS/STAT® software: you can now use the RMSTREG procedure to fit linear and log-linear models, and you can use the RMST option in PROC LIFETEST to estimate the restricted mean survival time and make comparisons between groups. The paper discusses the rationale behind the RMST-based approach, outlines its recent development, and uses examples to illustrate real-world applications of the RMSTREG and LIFETEST procedures.</p>
<p>Changbin Guo, SAS<br>
So Ying, SAS</p>
<p style="font-family: Arial,Helvetica,sans-serif; font-size: 90%; line-height: 300%; margin: -3em 0em 2em 0em;"><a href="https://github.com/sascommunities/sas-global-forum-2019">Access sample code</a></p>
<p>.</p>
Session 3013
en
jeff
<p>Survival analysis handles time-to-event data. Classical methods, such as the log-rank test and the Cox proportional hazards model, focus on the hazard function and are most suitable when the proportional hazards assumption holds. When it does not hold, restricted mean survival time (RMST) methods often apply. The RMST is the expected survival time subject to a specific time horizon, and it is an alternative measure to summarize the survival profile. RMST-based inference has attracted attention from practitioners for its capability to handle nonproportionality. This paper introduces RMST methods in SAS/STAT® software: you can now use the RMSTREG procedure to fit linear and log-linear models, and you can use the RMST option in PROC LIFETEST to estimate the restricted mean survival time and make comparisons between groups. The paper discusses the rationale behind the RMST-based approach, outlines its recent development, and uses examples to illustrate real-world applications of the RMSTREG and LIFETEST procedures.</p>
<p><a href="https://github.com/sascommunities/sas-global-forum-2019">Access sample code files now</a></p>
<p>Changbin Guo, SAS<br>
So Ying, SAS</p>
pdflatex
2019-04-12T11:15:39.000-04:00
2019-04-12T11:15:39.000-04:00
2019-02-28T14:16:33.000-05:00
application/pdf
restricted mean, survival analysis, SAS
2019-04-24T14:00:04.917-04:00
Changbin Guo and Yu Liang
Survival analysis handles time-to-event data. Classical methods, such as the log-rank test and the Cox proportional hazards model, focus on the hazard function and are most suitable when the proportional hazards assumption holds. When it does not hold, restricted mean survival time (RMST) methods often apply. The RMST is the expected survival time subject to a specific time horizon, and it is an alternative measure to summarize the survival profile. RMST-based inference has attracted attention from practitioners for its capability to handle nonproportionality. This paper introduces RMST methods in SAS/STAT® software: you can now use the RMSTREG procedure to fit linear and log-linear models, and you can use the RMST option in PROC LIFETEST to estimate the restricted mean survival time and make comparisons between groups. The paper discusses the rationale behind the RMST-based approach, outlines its recent development, and uses examples to illustrate real-world applications of the RMSTREG and LIFETEST procedures.
Access sample code files now
Changbin Guo, SAS
So Ying, SAS
Analyzing Restricted Mean Survival Time Using SAS/STAT®
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