Differences in surrogate threshold effect estimates between original and simplified correlation-based validation approaches

2015 ◽  
Vol 35 (7) ◽  
pp. 1049-1062 ◽  
Author(s):  
Christoph Schürmann ◽  
Wiebke Sieben
Keyword(s):  
Threshold Effect ◽  
Surrogate Threshold Effect

LDL-cholesterol differences predicted survival benefit in statin trials by the surrogate threshold effect (STE)

2009 ◽  
Vol 62 (3) ◽  
pp. 328-336 ◽  
Author(s):  
Kent R. Johnson ◽  
Nick Freemantle ◽  
Danielle M. Anthony ◽  
Marissa N.D. Lassere
Keyword(s):  
Survival Benefit ◽  
Ldl Cholesterol ◽  
Threshold Effect ◽  
Surrogate Threshold Effect

Surrogate threshold effect: an alternative measure for meta-analytic surrogate endpoint validation

2006 ◽  
Vol 5 (3) ◽  
pp. 173-186 ◽  
Author(s):  
Tomasz Burzykowski ◽  
Marc Buyse
Keyword(s):  
Surrogate Endpoint ◽  
Threshold Effect ◽  
Alternative Measure ◽  
Surrogate Threshold Effect

Event-Free Survival Is a Surrogate for Overall Survival in Patients Treated for Acute Myeloid Leukemia

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 3744-3744 ◽  
Author(s):  
Richard F. Schlenk ◽  
Hartmut Döhner ◽  
Konstanze Döhner ◽  
Arnold Ganser ◽  
Michael Heuser ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Treatment Effect ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Treatment Effects ◽  
Regression Line ◽  
Threshold Effect ◽  
Coefficient Of Determination ◽  
Surrogate Threshold Effect ◽  
Acute Myeloid

Abstract Purpose: We evaluated whether event-free survival (EFS) can be used as a surrogate for overall survival (OS) in patients treated for acute myeloid leukemia. Material: We carried out a meta-analysis of individual patient data from four randomized clinical trials carried out under the auspices of the German-Austrian Acute Myeloid Leukemia Study Group (AMLSG): AMLHD 98B (Schlenk et al. Leukemia 2004 18:1798-803; n=254), AMLSG 06-04 (NCT00151255, n=189), AMLSG 07-04 (NCT00151242, n=1,100) and AMLSG 12-09 (NCT01180322, n=268). Some of these trials addressed multiple therapeutic questions, which resulted in a total of 7 independent treatment comparisons. Methods: A two-level modelling approach was used to estimate the association between EFS and OS, and between the treatment effects on EFS and on OS. At the individual level, a copula was fitted to model the joint distribution of EFS and OS, and Spearman's rank correlation coefficient (rho) was used to quantify the association between the endpoints. At the trial-level, a linear regression was fitted through the estimated treatment effects (Weibull-model-based log hazard ratios) on EFS and OS, taking into account the estimation error. The coefficient of determination (R²) was used to quantify the association between the treatment effects. The surrogate threshold effect (STE) was estimated as the treatment effect on EFS that would predict a significant treatment effect on OS. Results: A total of n=1,811 patients were included in the analysis. Spearman's correlation coefficient was equal to 0.76 (standard error, SE, 0.015). The coefficient of determination (R²) of the linear regression between log hazard ratios on EFS and on OS was equal to 0.97 (SE 0.13). The intercept of the regression line was equal to -0.04 (SE 0.04) and the slope was equal to 0.80 (SE 0.21). The surrogate threshold effect was equal to 0.90. Using an alternative method of estimation of treatment effects, marginal proportional hazards models for EFS and OS, the R² was equal to 0.98 (SE 0.21), the intercept of the regression line was equal to -0.02 (SE 0.05), the slope was equal to 0.82 (SE 0.28), and the surrogate threshold effect was equal to 0.89. Further results for different subsets of patients, for example, those with activating FLT3 mutations, will be presented at the meeting. Interpretation: In this population of intensively-treated AML patients, there was a tight association between the treatment effect on EFS and OS, suggesting that the former can be used as a surrogate for the latter in clinical trials assessing the efficacy of new treatments. The surrogate threshold effect of about 0.90 and the regression analysis suggest that a reduction of at least 10% in the risk of an event would reliably predict a reduction of approximately 8% in the risk of death. Disclosures Schlenk: Celgene: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Teva: Honoraria, Research Funding; AROG: Honoraria, Research Funding; Amgen: Research Funding; Böhringer Ingelheim: Honoraria; Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Buyse:IDDI: Employment; Novartis: Research Funding. Burzykowski:IDDI: Employment; Novartis: Research Funding.

Progression-free survival (PFS) as a surrogate endpoint for overall survival (OS) in advanced/recurrent gastric cancer (AGC) treatment: Individual-patient-data (IPD) based meta-analysis of randomized trials.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16506-e16506
Author(s):  
Koji Oba ◽  
Xavier Paoletti ◽  
Yung-Jue Bang ◽  
Olivier Bouché ◽  
Michel Ducreux ◽  
...  
Keyword(s):  
Treatment Effects ◽  
Randomized Trials ◽  
Meta Analysis ◽  
Surrogate Endpoint ◽  
Threshold Effect ◽  
Patient Data ◽  
Individual Level ◽  
Measurable Disease ◽  
Surrogate Threshold Effect ◽  
The Individual

e16506 Background: In 2013, the GASTRIC (Global Advanced/Adjuvant Stomach Tumor Research through International Collaboration) evaluated the surrogacy of PFS based on IPD of 4,069 patients from 20 randomized trials of AGC. Treatment effects on PFS and on OS were only moderately correlated, and we could not validate PFS as a surrogate endpoint for OS. More recent trials, with refined inclusion criteria and higher standards for evaluating progression, may allow for a more accurate estimate of the correlation. The 2nd round of the GASTRIC sought to re-evaluate the surrogacy of PFS for OS in AGC. Methods: The GASTRIC database was updated with trials published after 2010 which used RECIST (Response Evaluation Criteria in Solid Tumors). Since the proportional hazards assumption was questionable for PFS, we primarily used mean-time ratio as a treatment effect measure, estimated by using the log-logistic model. Using the meta-analytic approach, correlations between PFS and OS at the individual level (Rindiv), and between treatment effects on PFS and on OS at the trial level (Rtrial), were estimated using Spearman’s rank-correlation and estimation-error-adjusted regression, respectively. Surrogate threshold effect was estimated as well. Results: We analyzed 10,912 patient data (1st round 4,069 patients from 20 trials and 2nd round 6,843 patients from 17 trials). Overall, moderate correlations were found at the individual level (Rindiv = 0.75, 95%CI = 0.75 to 0.76 in Hougaard copula) and at the trial level (Rtrial = 0.77, 95%CI = 0.32 to 1.00), respectively. Surrogate threshold effect was equal to 1.29, i.e., observing 29% increase in mean PFS time would predict a significant increase of the OS time. In the subgroup of patients with measurable disease in the 2nd round dataset (4,866 patients), Rtrial was higher and equal to 0.93 (95%CI = 0.70 to 1.00), with STE equal to 1.21. These results were same for 1st and 2nd line trials. Conclusions: The meta-analysis indicates a strong correlation between treatment effects (expressed as log-mean-ratios) on PFS and OS in patients with measurable disease.

The individual‐level surrogate threshold effect in a causal‐inference setting with normally distributed endpoints

2021 ◽  
Author(s):  
Wim Van der Elst ◽  
Ariel Alonso Abad ◽  
Hans Coppenolle ◽  
Paul Meyvisch ◽  
Geert Molenberghs
Keyword(s):  
Causal Inference ◽  
Threshold Effect ◽  
Individual Level ◽  
Surrogate Threshold Effect ◽  
The Individual ◽  
Normally Distributed

Disease-free survival as a surrogate for overall survival in neoadjuvant trials of gastroesophageal adenocarcinoma: Pooled analysis of individual patient data from randomized controlled trials.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 4533-4533
Author(s):  
Ulrich Ronellenfitsch ◽  
Katrin Jensen ◽  
Svenja Seide ◽  
Meinhard Kieser ◽  
Matthias Schwarzbach ◽  
...  
Keyword(s):  
Neoadjuvant Therapy ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Meta Analysis ◽  
Disease Free Survival ◽  
Surrogate Endpoint ◽  
Threshold Effect ◽  
Free Survival ◽  
Gastroesophageal Adenocarcinoma ◽  
Surrogate Threshold Effect

4533 Background: Disease-free survival (DFS) is an appealing surrogate endpoint for overall survival (OS) in trials on neoadjuvant or adjuvant cancer therapy, because it is available faster and with less follow-up effort. The aim of this study was to assess if DFS can be a valid surrogate endpoint for OS when comparing neoadjuvant treatment followed by surgery to surgery alone for gastroesophageal adenocarcinoma. Methods: Individual patient data (IPD) from eight randomized controlled trials (n = 1,126 patients) which compared neoadjuvant therapy followed by surgery with surgery alone for gastroesophageal adenocarcinoma were used for the analysis. Correlation between OS-time and DFS-time was calculated. Kaplan-Meier survival curves and corresponding hazard ratios (HRs) for treatment effects were separately determined for each trial. Subsequently, HRs were pooled in a meta-analysis using a random-effects model. An error-in-variables linear regression model was used to compare observed and predicted values. The minimum treatment effect on DFS necessary to predict a non-zero treatment effect on OS was estimated by calculating the surrogate threshold effect. Results: OS-time correlated strongly with DFS-time. HRs for OS and DFS were highly similar for all single trials. The meta-analysis yielded almost identical overall HRs for treatment effects on OS and DFS. The determination coefficient for the association between HRs for OS and DFS was 0.912 (95% confidence interval 0.75-1.0), indicating a strong trial-level surrogacy between OS and DFS. The surrogate threshold effect was calculated at 0.79, indicating that a future trial yielding a hazard ratio for the treatment effect on DFS < 0.79 could be expected with a 95% probability to yield a hazard ratio for the treatment effect on OS < 1. Conclusions: DFS and OS strongly correlate both after neoadjuvant therapy followed by surgery and after surgery alone for gastroesophageal adenocarcinoma. Likewise, the treatment effects on the two endpoints are very similar. Consequently, DFS can be regarded an appropriate surrogate endpoint for OS in trials on neoadjuvant therapy for gastroesophageal adenocarcinoma.

scholarly journals Validation of the Surrogate Threshold Effect for Change in BMD as a Surrogate Endpoint for Fracture Outcomes: the FNIH‐ASBMR SABRE Project

2021 ◽  
Author(s):  
Richard Eastell ◽  
Eric Vittinghoff ◽  
Li‐Yung Lui ◽  
Charles E. McCulloch ◽  
Imre Pavo ◽  
...  
Keyword(s):  
Surrogate Endpoint ◽  
Threshold Effect ◽  
Surrogate Threshold Effect

scholarly journals Progression-Free Survival as a Surrogate End Point for Overall Survival in First-Line Diffuse Large B-Cell Lymphoma: An Individual Patient–Level Analysis of Multiple Randomized Trials (SEAL)

2018 ◽  
Vol 36 (25) ◽  
pp. 2593-2602 ◽  
Author(s):  
Qian Shi ◽  
Norbert Schmitz ◽  
Fang-Shu Ou ◽  
Jesse G. Dixon ◽  
David Cunningham ◽  
...  
Keyword(s):  
Progression Free Survival ◽  
B Cell Lymphoma ◽  
Threshold Effect ◽  
First Line ◽  
Free Survival ◽  
Patient Level ◽  
Randomized Controlled ◽  
End Point ◽  
Large B Cell Lymphoma ◽  
Surrogate Threshold Effect

Purpose Overall survival (OS) is the definitive and best-established primary efficacy end point to evaluate diffuse large B-cell lymphoma (DLBCL) therapies, but it requires prolonged follow-up. An earlier end point assessed post-treatment would expedite clinical trial conduct and accelerate patient access to effective new therapies. Our objective was to formally evaluate progression-free survival (PFS) and PFS at 24 months (PFS24) as surrogate end points for OS in first-line DLBCL. Patients and Methods Individual patient data were analyzed from 7,507 patients from 13 multicenter randomized controlled trials of active treatment in previously untreated DLBCL, published after 2002, with sufficient PFS data to predict treatment effects on OS. Trial-level surrogacy examining the correlation of treatment effect estimates of PFS/PFS24 and OS was evaluated using both linear regression ( R2WLS) and Copula bivariable ( R2Copula) models. Prespecified criteria for surrogacy required either R2WLS or R2Copula ≥ 0.80 and neither < 0.7, with lower-bound 95% CI > 0.60. Results Trial-level surrogacy for PFS was strong ( R2WLS = 0.83; R2Copula = 0.85) and met the predefined criteria for surrogacy. At the patient level, PFS strongly correlated with OS. The surrogate threshold effect had a hazard ratio of 0.89. Surrogacy was consistent across comparisons with or without rituximab and with rituximab maintenance trials. Trial-level surrogacy for PFS24 was relatively strong ( R2WLS = 0.77; R2Copula = 0.78) but did not meet prespecified criteria. At the patient level, PFS24 significantly correlated with OS. The surrogate threshold effect had an odds ratio of 1.51. Conclusion This large pooled analysis of individual patient data supports PFS as a surrogate end point for OS in future randomized controlled trials evaluating chemoimmunotherapy in DLBCL. Use of this end point may expedite therapeutic development with the intent of bringing novel therapies to this patient population years before OS results are mature.

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