scholarly journals Analysis of restricted mean survival time for length-biased data

Biometrics ◽  
2017 ◽  
Vol 74 (2) ◽  
pp. 575-583 ◽  
Author(s):  
Chi Hyun Lee ◽  
Jing Ning ◽  
Yu Shen
Keyword(s):  
Survival Time ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

Estimation on conditional restricted mean survival time with counting process

2020 ◽  
pp. 1-15
Author(s):  
Junshan Qiu ◽  
Dali Zhou ◽  
H.M. Jim Hung ◽  
John Lawrence ◽  
Steven Bai
Keyword(s):  
Survival Time ◽  
Counting Process ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

A Model-Independent Method to Determine Restricted Mean Survival Time in the Analysis of Survival Curves

2019 ◽  
Vol 2 (1) ◽  
pp. 66-68 ◽  
Author(s):  
Andrea Messori ◽  
Vera Damuzzo ◽  
Laura Agnoletto ◽  
Luca Leonardi ◽  
Marco Chiumente ◽  
...  
Keyword(s):  
Survival Time ◽  
Independent Method ◽  
Survival Curves ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time ◽  
Model Independent

scholarly journals Extrapolating Parametric Survival Models in Health Technology Assessment Using Model Averaging: A Simulation Study

2021 ◽  
Vol 41 (4) ◽  
pp. 476-484
Author(s):  
Daniel Gallacher ◽  
Peter Kimani ◽  
Nigel Stallard
Keyword(s):  
Survival Time ◽  
Survival Data ◽  
Mean Squared Error ◽  
Model Averaging ◽  
Information Criterion ◽  
Health Technology ◽  
Weighted Averaging ◽  
Health Technologies ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

Previous work examined the suitability of relying on routine methods of model selection when extrapolating survival data in a health technology appraisal setting. Here we explore solutions to improve reliability of restricted mean survival time (RMST) estimates from trial data by assessing model plausibility and implementing model averaging. We compare our previous methods of selecting a model for extrapolation using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Our methods of model averaging include using equal weighting across models falling within established threshold ranges for AIC and BIC and using BIC-based weighted averages. We apply our plausibility assessment and implement model averaging to the output of our previous simulations, where 10,000 runs of 12 trial-based scenarios were examined. We demonstrate that removing implausible models from consideration reduces the mean squared error associated with the restricted mean survival time (RMST) estimate from each selection method and increases the percentage of RMST estimates that were within 10% of the RMST from the parameters of the sampling distribution. The methods of averaging were superior to selecting a single optimal extrapolation, aside from some of the exponential scenarios where BIC already selected the exponential model. The averaging methods with wide criterion-based thresholds outperformed BIC-weighted averaging in the majority of scenarios. We conclude that model averaging approaches should feature more widely in the appraisal of health technologies where extrapolation is influential and considerable uncertainty is present. Where data demonstrate complicated underlying hazard rates, funders should account for the additional uncertainty associated with these extrapolations in their decision making. Extended follow-up from trials should be encouraged and used to review prices of therapies to ensure a fair price is paid.

Restricted mean survival time as a summary measure of time‐to‐event outcome

2020 ◽  
Vol 19 (4) ◽  
pp. 436-453 ◽  
Author(s):  
Takahiro Hasegawa ◽  
Saori Misawa ◽  
Shintaro Nakagawa ◽  
Shinichi Tanaka ◽  
Takanori Tanase ◽  
...  
Keyword(s):  
Survival Time ◽  
Time To Event ◽  
Summary Measure ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

A permutation test based on the restricted mean survival time for comparison of net survival distributions in non-proportional excess hazard settings

2019 ◽  
Vol 29 (6) ◽  
pp. 1612-1623
Author(s):  
Anna Wolski ◽  
Nathalie Grafféo ◽  
Roch Giorgi ◽  
Keyword(s):  
Survival Time ◽  
Proportional Hazards ◽  
Permutation Test ◽  
Survival Times ◽  
Test Statistic ◽  
Specific Test ◽  
Net Survival ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time ◽  
Type Test

Net survival is used in epidemiological studies to assess excess mortality due to a given disease when causes of death are unreliable. By correcting for the general population mortality, it allows comparisons between regions or periods and thus evaluation of health policies. The Pohar-Perme non-parametric estimator of net survival has been recently proposed, soon followed by an appropriate log-rank-type test. However, log-rank tests are known to be under-optimal in non-proportional settings (e.g. crossing of the hazard functions). In classical survival analysis, one solution is to compare the restricted mean survival times. A difference in restricted mean survival time represents a life benefit or loss over the studied period. In the present article the restricted mean net survival time was used to derive a specific test statistic to compare net survivals in proportional and non-proportional hazards settings. The new test was generalized to more than two groups and to stratified analysis. The test performance was assessed on simulation study, compared to the log-rank-type test, and its use illustrated on a population-based colorectal cancer registry. The new test for net survival comparisons proved robust to non-proportionality and well-performing in proportional hazards situations. Furthermore, it is also suited to the classical survival framework.

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