scholarly journals Weighted estimators of the complier average causal effect on restricted mean survival time with observed instrument–outcome confounders

2020 ◽  
Author(s):  
Sai H. Dharmarajan ◽  
Yun Li ◽  
Douglas Lehmann ◽  
Douglas E. Schaubel
Keyword(s):  
Survival Time ◽  
Causal Effect ◽  
Mean Survival Time ◽  
Average Causal Effect ◽  
Restricted Mean Survival Time ◽  
Complier Average Causal Effect

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.

Paracetamol bei Rückenschmerzen nicht empfohlen

2020 ◽  
Vol 24 (02) ◽  
pp. 54-55
Author(s):  
Arne Vielitz
Keyword(s):  
Randomized Controlled Trial ◽  
Low Back Pain ◽  
Causal Effect ◽  
Controlled Trial ◽  
Low Back ◽  
Effect Analysis ◽  
Acute Low Back Pain ◽  
Randomized Controlled ◽  
Average Causal Effect ◽  
Complier Average Causal Effect

Schreijenberg M, Lin CC, McLachlan AJ et al. Paracetamol is Ineffective for Acute Low Back Pain even for Patients Who Comply with Treatment: Complier Average Causal Effect Analysis of a Randomized Controlled Trial. Pain 2019; 160: 2848–2854. doi: 10.1097/j.pain.0000000000001685

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

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
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