scholarly journals Thyroid disease and hepatocellular carcinoma survival. A Danish nationwide cohort study

2021 ◽  
Author(s):  
Linda Skibsted Kornerup ◽  
Frederik Kraglund ◽  
Ulla Feldt-Rasmussen ◽  
Peter Jepsen ◽  
Hendrik Vilstrup
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cohort Study ◽  
Survival Time ◽  
Thyroid Disease ◽  
Animal Studies ◽  
Hormone Treatment ◽  
Mean Survival Time ◽  
Mortality Hazard ◽  
Restricted Mean Survival Time

Introduction Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer mortality worldwide. Recent animal studies suggest that thyroid hormone treatment improves HCC prognosis. The aim of this study was to describe the association between thyroid disease and HCC prognosis in humans. Methods We performed a nationwide cohort study including all persons with an HCC diagnosis from 2000-2018. Patients’ age, sex, HCC treatment, and diagnoses of thyrotoxicosis, nontoxic goitre, and myxoedema, were obtained from Danish national healthcare registries. We used regression models to examine the association between thyroid disease and mortality hazard and restricted mean survival time after HCC diagnosis, adjusting for confounding by sex and age. Results We included 4,812 patients with HCC and 107 patients with thyroid disease. Median follow-up time was 5 months (total 5,985 person-years). The adjusted mortality hazard ratio was 0.68 (95% CI 0.47-0.96) for thyrotoxicosis and 0.60 (95% CI 0.41-0.88) for nontoxic goitre. The restricted mean survival time during the five years following HCC diagnosis was 6.8 months (95% CI 1.1–12.6) longer for HCC patients with thyrotoxicosis than for patients without thyroid disease, and it was 6.9 months (95% CI 0.9–12.9) longer for HCC patients with nontoxic goitre than for patients without thyroid disease. Conclusions In this large nationwide cohort study, thyrotoxicosis and nontoxic goitre were associated with prolonged HCC survival.

OP236 Evidence Synthesis Of Time-To-Event Outcomes In The Presence Of Non-Proportional Hazards

2021 ◽  
Vol 37 (S1) ◽  
pp. 8-8
Author(s):  
Suzanne Freeman ◽  
Nicola Cooper ◽  
Alex Sutton ◽  
Michael Crowther ◽  
James Carpenter ◽  
...  
Keyword(s):  
Survival Time ◽  
Simulation Study ◽  
Proportional Hazards ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Clinical Effectiveness ◽  
Time To Event ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

IntroductionSynthesis of clinical effectiveness is a well-established component of health technology assessment (HTA) combining data from multiple trials to obtain an overall pooled estimate of clinical effectiveness, which may inform an associated economic evaluation. Time-to-event outcomes are often synthesized using effect measures from Cox proportional hazards models assuming a constant hazard ratio over time. However, where treatment effects vary over time an assumption of proportional hazards is not always valid. Several methods have been proposed for synthesizing time-to-event outcomes in the presence of non-proportional hazards. However, guidance on choosing between these methods and the implications for HTA is lacking.MethodsWe applied five methods for estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption to a network of melanoma trials, reporting overall survival: restricted mean survival time, an accelerated failure time generalized gamma model, piecewise exponential, fractional polynomial and Royston-Parmar models. We conducted a simulation study to compare these five methods. Simulated individual patient data was generated from a mixture Weibull distribution assuming a treatment-time interaction. Each simulated meta-analysis consisted of five trials with varying numbers of patients and length of follow-up across trials. For each model fitted to each dataset, we calculated the restricted mean survival time at the end of observed follow-up and following extrapolation to a 20-year time horizon.ResultsAll models fitted the melanoma data reasonably well with some variation in the treatment rankings and differences in the survival curves. The simulation study demonstrated the potential for different conclusions from different modelling approaches.ConclusionsThe restricted mean survival time, generalized gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can all accommodate non-proportional hazards and differing lengths of trial follow-up within an evidence synthesis of time-to-event outcomes. Further work is needed in this area to extend the simulation study to the network meta-analysis setting and provide guidance on the key considerations for informing model choice for the purposes of HTA.

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

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

Event free survival in adults with relapsed ALL who underwent front-line therapy with CALGB 10403.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e19005-e19005
Author(s):  
Suravi Raychaudhuri ◽  
Ilana Yurkiewicz ◽  
Gabriel N. Mannis ◽  
Bruno C. Medeiros ◽  
Steve E. Coutre ◽  
...  
Keyword(s):  
Overall Survival ◽  
Cohort Study ◽  
Survival Time ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Event Free Survival ◽  
Front Line ◽  
Free Survival ◽  
First Relapse

e19005 Background: CALGB 10403 is a pediatric-inspired ALL regimen that has recently been shown to have improved survival rates in adolescents and young adults with ALL when compared to historical outcomes with traditional adult ALL regimens (Stock et. al, 2019). Methods: This is a retrospective cohort study of ALL patients who received induction CALGB 10403 at Stanford University (both on and off trial), achieved CR1, and subsequently relapsed. Primary outcome of interest was event free survival from time of diagnosis. Events were defined as relapse or death. Secondary outcomes were overall survival and event free survival from first relapse. Patients were censored at time of last clinical follow up. Results: 25 patients met inclusion criteria and received front-line CALGB 10403 from April 2010 to September 2018. At the time of initial diagnosis median age was 30 years (range 18 – 39 years). 68% of patients were male. 48% of patients were overweight and 40% were obese. 76% of patients had precursor B cell ALL while 24% had T cell ALL. 12% had CNS disease at diagnosis. 36% of patients had WBC greater than 30k. 12% of patients had CRLF2 rearrangement. 12% of patients were MRD positive after first induction. 20% of patients received rituximab. Median event free survival time from diagnosis was 20 months (range 3 – 79 months) and median overall survival time was 53 months. Blinatumomab was the most common salvage therapy after 1st relapse, followed by inotuzumab. 15 patients (60%) achieved CR2, of which 4 (27%) were MRD positive after 2nd induction. 15 patients (60%) went to HSCT. Of the patients who achieved CR2, 8 relapsed for a second time. Median event free survival time after first relapse was 23 months. Survival 1 year after relapse was 60%. 11 of the 25 patients were alive at last follow up. Median follow up time of survivors was 6 years. Conclusions: This is a descriptive retrospective cohort study of adult patients in a real world setting who received CALGB 10403 induction and subsequently relapsed. Compared to other studies of relapsed ALL patients who were induced with traditional chemotherapy (Fielding et. al, 2007), survival 1 year after relapse was much higher (60% vs. 22%). As CALGB 10403 becomes an increasingly common induction regimen for AYA and adults with ALL, further outcomes study is required.[Table: see text]

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