scholarly journals Survival (time to event) data II

BMJ ◽  
2010 ◽  
Vol 341 (jul14 1) ◽  
pp. c3665-c3665
Author(s):  
P. Sedgwick
Keyword(s):  
Survival Time ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

Survival (time to event) data: median survival times

BMJ ◽  
2011 ◽  
Vol 343 (aug10 3) ◽  
pp. d4890-d4890 ◽  
Author(s):  
P. Sedgwick ◽  
K. Joekes
Keyword(s):  
Survival Time ◽  
Median Survival ◽  
Event Data ◽  
Survival Times ◽  
Time To Event ◽  
Time To Event Data

scholarly journals A univariable and multivariable analysis of time-to-event data based on restricted mean survival time: A combination with traditional survival methods.

2019 ◽  
Author(s):  
Qiao Huang ◽  
Jun Lyv ◽  
Bing-hui Li ◽  
Lin-lu Ma ◽  
Tong Deng ◽  
...  
Keyword(s):  
Survival Time ◽  
Cox Regression ◽  
Multivariable Analysis ◽  
Hazard Ratio ◽  
Event Data ◽  
Time To Event ◽  
Mean Survival Time ◽  
Time To Event Data ◽  
Effect Measure ◽  
Restricted Mean Survival Time

Abstract Background Hazard ratio is considered as an appropriate effect measure of time-to-event data. However, hazard ratio is only valid when proportional hazards (PH) assumption is met. The use of the restricted mean survival time (RMST) is proposed and recommended without limitation of PH assumption. Method 4405 osteosarcomas were captured from Surveillance, Epidemiology and End Results Program Database. Traditional survival analyses and RMST-based analyses were integrated into a flowchart and applied for univariable and multivariable analyses, using hazard ratio (HR) and difference in RMST (survival time lost or gain, STL or STG) as effect measures. The relationship between difference in RMST and HR were explored when PH assumption was and was not met, respectively. Results In univariable analyses, using difference in RMST calculated by Kaplan-Meier methods as reference, pseudo-value regressions (R2=0.99) and inverse probability of censoring probability (IPCW) regressions with group-specific weights (R2=1.00) provided more consistent estimation on difference in RMST than IPCW with individual weights (R2=0.09). In multivariable analysis, age (HR:1.03, STL: 3.86 months), diagnosis in 1970~1980s (HR:1.39 STL:27.49 months), metastasis (HR:4.47, STL: 202 months), surgery (HR:0.58, SLG:35.55 months) and radiation (HR:1.46, SLT:44.65 months), met PH assumption and were main independent factors for overall survival. In both univariable and multivariable variables, a robust negative logarithmic linear relationship between HRs estimated by Cox regression and differences in RMST by pseudo-value regressions was only observed when PH assumption was hold (Difference in RMST = -109.3✕ln (HR) - 0.83, R² = 0.97, and Difference = -127.7✕ln (HR) – 9.49, R² = 0.93, respectively.) Conclusion The flowchart will be intuitive and helpful to instruct appropriate use of RMST based and traditional methods. RMST-based methods provided an absolute effect measure to inspect effects of covariates on survival time and promote evidence communication with HR. Difference in RMST should be reported with hazard ratio routinely.

scholarly journals A univariable and multivariable analysis of right censored time-to-event data based on restricted mean survival time: A combination with traditional survival methods.

2020 ◽  
Author(s):  
Qiao Huang ◽  
Jun Lyv ◽  
Bing-hui Li ◽  
Lin-lu Ma ◽  
Tong Deng ◽  
...  
Keyword(s):  
Survival Time ◽  
Cox Regression ◽  
Multivariable Analysis ◽  
Hazard Ratio ◽  
Event Data ◽  
Time To Event ◽  
Mean Survival Time ◽  
Time To Event Data ◽  
Effect Measure ◽  
Restricted Mean Survival Time

Abstract Background Hazard ratio is considered as an appropriate effect measure of time-to-event data. However, hazard ratio is only valid when proportional hazards (PH) assumption is met. The use of the restricted mean survival time (RMST) is proposed and recommended without limitation of PH assumption. Method 4405 osteosarcomas were captured from Surveillance, Epidemiology and End Results Program Database. Traditional survival analyses and RMST-based analyses were integrated into a flowchart and applied for univariable and multivariable analyses, using hazard ratio (HR) and difference in RMST (survival time lost or gain, STL or STG) as effect measures. The relationship between difference in RMST and HR were explored when PH assumption was and was not met, respectively. Results In group comparison and univariable regressions, using difference in RMST calculated by Kaplan-Meier methods as reference, pseudo-value regressions (R2=0.99) and inverse probability of censoring probability (IPCW) regressions with group-specific weights (R2=1.00) provided more consistent estimation on difference in RMST than IPCW with individual weights (R2=0.09). In multivariable analysis, age (HR:1.03, STL: 3.86 months), diagnosis in 1970~1980s (HR:1.39 STL:27.49 months), metastasis (HR:4.47, STL: 202 months), surgery (HR:0.58, SLG:35.55 months) and radiation (HR:1.46, SLT:44.65 months) met PH assumption and were main independent factors for overall survival. In both univariable and multivariable variables, a robust negative logarithmic linear relationship between HRs estimated by Cox regression and differences in RMST by pseudo-value regressions was only observed when PH assumption was hold. Conclusion The flowchart will be intuitive and helpful to instruct appropriate use of RMST based and traditional methods. RMST-based methods provided an absolute effect measure to inspect effects of covariates on survival time and promote evidence communication with HR. Difference in RMST should be reported with hazard ratio routinely.

Survival (time to event) data I

BMJ ◽  
2010 ◽  
Vol 341 (jul07 1) ◽  
pp. c3537-c3537
Author(s):  
P. Sedgwick
Keyword(s):  
Survival Time ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

scholarly journals A novel estimator of survival without assumption of censored survival time

2021 ◽  
Author(s):  
Shee-Ping Chen
Keyword(s):  
Survival Time ◽  
Survival Function ◽  
Event Data ◽  
Time To Event ◽  
Survival Probabilities ◽  
Time To Event Data ◽  
Kaplan Meier

Abstract The Kaplan-Meier estimator is commonly used to analyze time-to-event data, but it may over estimate the survival function when censored events occur. Several methods have been developed to adjust the traditional Kaplan-Meier estimator. Most of these adjusted methods are based on their assumptions for survival time of censored events. We here propose a novel estimator of survival without assumption of censored survival time, and it gives sensible estimates of survival probabilities in various censoring conditions.

Survival (time to event) data: censored observations

BMJ ◽  
2011 ◽  
Vol 343 (aug03 3) ◽  
pp. d4816-d4816
Author(s):  
P. Sedgwick
Keyword(s):  
Survival Time ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Censored Observations

scholarly journals A univariable and multivariable analysis of right censored time-to-event data based on restricted mean survival time: A combination with traditional survival methods.

2020 ◽  
Author(s):  
Qiao Huang ◽  
Jun Lyv ◽  
Bing-hui Li ◽  
Lin-lu Ma ◽  
Tong Deng ◽  
...  
Keyword(s):  
Overall Survival ◽  
Survival Time ◽  
Multivariable Analysis ◽  
Group Comparison ◽  
Event Data ◽  
Time To Event ◽  
Survival Analyses ◽  
Mean Survival Time ◽  
Time To Event Data ◽  
Restricted Mean Survival Time

Abstract Background: Many traditional survival analyses and restricted mean survival time (RMST) based analyses have been proposed for dealing with right censored time-to-event data. It is necessary to sort out the conditions and relationship among these methods for instruction. Comparison between hazard ratio (HR) and RMST in a study may promote appropriate understanding and application of the two effect measures.Method : Traditional survival analyses and RMST-based analyses were integrated into a flowchart and applied for univariable and multivariable analyses, RMST-based analyses included group comparison for difference in RMST, pseudo-value (PV) regressions, inverse probability of censoring probability (IPCW) regressions with group-specific weights and individual weights with homogeneity of censoring mechanism assumption. 4405 osteosarcomas were captured from Surveillance, Epidemiology and End Results Program Database. HR and difference in RMST (survival time lost or gain, STL or STG) were considered as effect measures and the effect of all included covariates on overall survival were reported for comparison. The relationship between difference in RMST and HR were explored when proportional hazard (PH) assumption was and was not met, respectively. Results: In group comparison and univariable regressions, using difference in RMST calculated by Kaplan-Meier methods as reference, PV regressions (R2=0.99) and IPCW regressions with group-specific weights (R2=1.00) provided more consistent estimation on difference in RMST than IPCW with individual weights (R2=0.09). In multivariable analysis, age (HR:1.03, STL: 3.86 months), diagnosis in 1970~1980s (HR:1.39 STL:27.49 months), metastasis (HR:4.47, STL: 202 months), surgery (HR:0.58, SLG:35.55 months) and radiation (HR:1.46, SLT:44.65 months) met PH assumption and were main independent factors for overall survival. In both univariable and multivariable variables, a robust negative logarithmic linear relationship between HRs estimated by Cox regression and differences in RMST by pseudo-value regressions was only observed when PH assumption was hold. Conclusion: The flowchart may be intuitive and helpful to instruct appropriate use of RMST based and traditional methods. PH assumption and homogeneity of censoring mechanism assumption will determine the appropriate selection of these method. HR and difference in RMST can be reported comprehensively.

scholarly journals Survival (time to event) data II

BMJ ◽  
2014 ◽  
Vol 349 (sep26 13) ◽  
pp. g5918-g5918
Keyword(s):  
Survival Time ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data
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