scholarly journals Tobit regression for modeling mean survival time using data subject to multiple sources of censoring

2018 ◽  
Vol 17 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Qi Gong ◽  
Douglas E. Schaubel
Keyword(s):  
Survival Time ◽  
Tobit Regression ◽  
Multiple Sources ◽  
Mean Survival Time ◽  
Using Data ◽  
Data Subject

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.

scholarly journals Laryngeal Neuroendocrine Carcinomas: A Retrospective Study of 14 Cases

2015 ◽  
Vol 2015 ◽  
pp. 1-4 ◽  
Author(s):  
Yingying Zhu ◽  
Liming Gao ◽  
Yunxiao Meng ◽  
Wenwen Diao ◽  
Xiaoli Zhu ◽  
...  
Keyword(s):  
Survival Time ◽  
Neuroendocrine Carcinoma ◽  
Clinical Manifestations ◽  
Small Cell ◽  
Atypical Carcinoid ◽  
Small Cell Neuroendocrine Carcinoma ◽  
Typical Carcinoid ◽  
Mean Survival Time ◽  
The Mean ◽  
Neuroendocrine Carcinomas

Laryngeal neuroendocrine carcinomas (LNECs) are rare and highly heterogeneous which present a wide spectrum of pathological and clinical manifestations. Fourteen patients with histologically demonstrated LNEC were collected and analyzed retrospectively. The 14 cases were classified into 3 subtypes: typical carcinoid in 2, atypical carcinoid in 5, and small cell neuroendocrine carcinoma in 7. The mean survival time of the 14 patients in this study was 112.5 months (95% CI, 81.5–143.6). Surgeries were performed for 2 patients of typical carcinoid, and they were alive with no evidence of recurrence after 24 and 47 months of follow-ups. Patients in the atypical carcinoid group were treated with surgeries and postoperative radiotherapy. After 58.4 months of follow-ups (range: 9–144), 2 patients showed no evidence of disease and 1 was lost to follow-up after 72 months. The other 2 patients died of other unrelated diseases. In the small cell neuroendocrine carcinoma group, a combination of chemotherapy and radiotherapy was applied. The mean survival time was 79.7 months (95% CI, 37.9–121.4), and the 5-year survival rate was 53.6%. In conclusion, the clinical behaviors, treatment protocols, and prognosis are different for each subtype of LNECs.

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

scholarly journals Efficacy of First-Third generation EGFR inhibitor combined with chemotherapy as first-line treatment for advanced lung adenocarcinoma in a real-world setting

2021 ◽  
Author(s):  
Ming-Wei Chen Ming-Wei Chen ◽  
An-Tai He . ◽  
Yi Pei .
Keyword(s):  
Lung Adenocarcinoma ◽  
Survival Time ◽  
Progression Free Survival ◽  
Egfr Mutations ◽  
Combination Group ◽  
First Line ◽  
Free Survival ◽  
Mean Survival Time ◽  
First Line Therapy ◽  
To Receive

Abstract BackgroundTo explore the optimal treatment strategy for patients who harbor sensitive EGFR mutations, a head-to-head study was performed to compare chemotherapy and gefitinib-erlotinip, osimertinib treatment in combination or with either agent alone as first-line therapy, in terms of efficacy and safety.MethodsA total of 200 untreated patients with advanced lung adenocarcinoma who harbored sensitive EGFR mutations were randomly assigned to receive gefitinib-erlotinip combined with pemetrexed and carboplatin group, gefitinib-erlotinip osimertinib combined with pemetrexed and carboplatin group, pemetrexed plus carboplatin alone group, or gefitinib-erlotinip alone group, osimertinib alone group.ResultsThe progression-free survival (PFS) of patients in the gefitinib-erlotinip combination group Mean Survival Time PFS 22.00 month,95%CI[16.29,27.70] and osimertinib gefitinib-erlotinip combination group Mean Survival Time PFS 40.00 month,95%CI[28.12,51.87]was longer than that of patients in the chemotherapy alone group PFS10,81 months, 95% CI,[ 8.99–12.64],gefitinib-erlotinip alone group PFS14.00 month.95%CI[11.98-20.01], osimertinib alone group PFS 26.66 month 95%CI[24.77-29.22].The gefitinib-erlotinip osimertinib combinational resulted in longer overall survival (OS) than chemotherapy alone (HR = 0.46, p = 0.016) or gefitinib-erlotinip alone (HR = 0.36, p = 0.01). osimertinib alone (HR = 0.26, p = 0.01).ConclusionsOur finding suggested that treatment with pemetrexed plus carboplatin combined with gefitinib-erlotinip and pemetrexed plus carboplatin combined with gefitinib-erlotinip osimertinib group could provide better survival benefits for patients with lung adenocarcinoma harboring sensitive EGFR mutations.

Effect of dietary alpha-linolenate/linoleate balance on mean survival time, incidence of stroke and blood pressure of spontaneously hypertensive rats

Life Sciences ◽  
1988 ◽  
Vol 43 (25) ◽  
pp. 2067-2075 ◽  
Author(s):  
Teruhiko Shimokawa ◽  
Atsuko Moriuchi ◽  
Takamitsu Hori ◽  
Masaki Saito ◽  
Yukio Naito ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Survival Time ◽  
Spontaneously Hypertensive Rats ◽  
Hypertensive Rats ◽  
Spontaneously Hypertensive ◽  
Mean Survival Time
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