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 Using Median Survival in Meta-analysis of Experimental Time-to-event Data

2021 ◽  
Author(s):  
Theodore C Hirst ◽  
Emily S. Sena ◽  
Malcolm R. Macleod
Keyword(s):  
Median Survival ◽  
Meta Analysis ◽  
Summary Statistics ◽  
Event Data ◽  
Time To Event ◽  
Preclinical Research ◽  
Time To Event Data ◽  
Hazard Ratios ◽  
Large Numbers ◽  
Meta Regression

Abstract Background: Time-to-event data is frequently reported in both clinical and preclinical research spheres. Systematic review and meta-analysis is a tool that can help to identify pitfalls in preclinical research conduct and reporting that can help to improve translational efficacy. However, pooling of studies using hazard ratios (HR) is cumbersome especially in preclinical meta-analyses including large numbers of small studies. Median survival is a much simpler metric although because of some limitations, which may not apply to preclinical data, it is generally not used in survival meta-analysis. We aimed to appraise its performance when compared with hazard ratio-based meta-analysis when pooling large numbers of small, imprecise studies.Methods: We simulated a survival dataset with features representative of a typical preclinical survival meta-analysis, including with influence of a treatment and a number of covariates. We calculated individual patient data-based hazard ratios and median survival ratios (MSR), comparing the summary statistics directly and their performance at Random-effects meta-analysis. Finally, we compared their sensitivity to detect associations between treatment and influential covariates at meta-regression.Results: There was in imperfect correlation between MSR and HR, although opposing direction of treatment effects between summary statistics appeared not to be a major issue. Precision was more conservative for HR than MSR, meaning that estimates of heterogeneity were lower. There was a slight sensitivity advantage for MSR at meta-analysis and meta-regression, although power was low in all circumstances.Conclusions: MSR appears to be a valid summary statistic for use in meta-analysis of small, imprecise experimental studies. It is computationally more straightforward and quicker to approximate than HR. While assessment of study precision and therefore weighting is less reliable, MSR appears to perform favourably during meta-analysis. Sensitivity of meta-regression was low for this set of parameters, so pooling of treatments to increase sample size may be required to ensure confidence in preclinical survival meta-regressions.

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 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

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 Using median survival in meta-analysis of experimental time-to-event data

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Theodore C. Hirst ◽  
Emily S. Sena ◽  
Malcolm R. Macleod
Keyword(s):  
Median Survival ◽  
Meta Analysis ◽  
Summary Statistics ◽  
Event Data ◽  
Time To Event ◽  
Preclinical Research ◽  
Time To Event Data ◽  
Hazard Ratios ◽  
Large Numbers ◽  
Meta Regression

Abstract Background Time-to-event data is frequently reported in both clinical and preclinical research spheres. Systematic review and meta-analysis is a tool that can help to identify pitfalls in preclinical research conduct and reporting that can help to improve translational efficacy. However, pooling of studies using hazard ratios (HRs) is cumbersome especially in preclinical meta-analyses including large numbers of small studies. Median survival is a much simpler metric although because of some limitations, which may not apply to preclinical data, it is generally not used in survival meta-analysis. We aimed to appraise its performance when compared with hazard ratio-based meta-analysis when pooling large numbers of small, imprecise studies. Methods We simulated a survival dataset with features representative of a typical preclinical survival meta-analysis, including with influence of a treatment and a number of covariates. We calculated individual patient data-based hazard ratios and median survival ratios (MSRs), comparing the summary statistics directly and their performance at random-effects meta-analysis. Finally, we compared their sensitivity to detect associations between treatment and influential covariates at meta-regression. Results There was an imperfect correlation between MSR and HR, although the opposing direction of treatment effects between summary statistics appeared not to be a major issue. Precision was more conservative for HR than MSR, meaning that estimates of heterogeneity were lower. There was a slight sensitivity advantage for MSR at meta-analysis and meta-regression, although power was low in all circumstances. Conclusions We believe we have validated MSR as a summary statistic for use in a meta-analysis of small, imprecise experimental survival studies—helping to increase confidence and efficiency in future reviews in this area. While assessment of study precision and therefore weighting is less reliable, MSR appears to perform favourably during meta-analysis. Sensitivity of meta-regression was low for this set of parameters, so pooling of treatments to increase sample size may be required to ensure confidence in preclinical survival meta-regressions.

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