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.

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.

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Surrogate endpoints for overall survival (OS) in head and neck squamous cell carcinoma (HNSCC): Evaluation using individual data of 23,737 patients

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.6035 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 6035-6035

Author(s):

S. Michiels ◽

A. Le Maître ◽

M. Buyse ◽

T. Burzykowski ◽

J. Bogaerts ◽

...

Keyword(s):

Correlation Coefficient ◽

Treatment Effects ◽

Randomized Trials ◽

Correlation Coefficients ◽

Surrogate Endpoint ◽

Bivariate Distribution ◽

Surrogate Endpoints ◽

Strongly Correlated ◽

Individual Level ◽

The Individual

6035 Background: The gold standard endpoint in randomized trials of HNSCC is OS. Our objective was to study if event-free survival (EFS) or loco-regional control (LRC) could be good surrogate endpoints to estimate the effect of radiotherapy (RT) and chemotherapy (CT) on OS. This would permit to decrease the duration and cost of the development of new treatments for HNSCC. Methods: EFS is the time from randomization to first event (loco-regional, distant recurrence or death), LRC the time from randomization to first loco-regional event. Individual patient data from two meta-analyses (MARCH; Bourhis, Lancet 2006, MACH-NC; Bourhis, ASCO 2004) were used. At the individual level, the rank correlation coefficient ρ between the surrogate endpoint (EFS or LRC) and OS was estimated from the bivariate distribution of these endpoints. At the trial level, the correlation coefficient R between treatment effects (estimated by log hazard ratios) on the surrogate endpoint and OS was estimated from a linear regression. EFS and LRC would be acceptable surrogates only if the correlation coefficients ρ and R were close to 1. Results: At the individual level, EFS was more strongly correlated with OS than LRC. For RT, treatment effects on both LRC and EFS were strongly correlated with those on OS. For CT, the correlation coefficients between treatment effects on EFS and OS were larger than those between LRC and OS. Conclusions: The preliminary analysis indicates that EFS can be used as a surrogate for OS to evaluate the treatment effect in randomized trials of patients with HNSCC. LRC is a possible alternative in RT alone trials. Unrestricted grants from ARC, LNCC, PHRC, Sanofi-Aventis. [Table: see text] [Table: see text]

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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.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.4533 ◽

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.

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Extending the I-squared statistic to describe treatment effect heterogeneity in cluster, multi-centre randomized trials and individual patient data meta-analysis

Statistical Methods in Medical Research ◽

10.1177/0962280220948550 ◽

2020 ◽

pp. 096228022094855

Author(s):

Karla Hemming ◽

James P Hughes ◽

Joanne E McKenzie ◽

Andrew B Forbes

Keyword(s):

Randomized Trial ◽

Treatment Effect ◽

Treatment Effects ◽

Randomized Trials ◽

Individual Patient Data ◽

Meta Analysis ◽

Cluster Randomized Trial ◽

Patient Data ◽

Treatment Effect Heterogeneity ◽

Effect Heterogeneity

Treatment effect heterogeneity is commonly investigated in meta-analyses to identify if treatment effects vary across studies. When conducting an aggregate level data meta-analysis it is common to describe the magnitude of any treatment effect heterogeneity using the I-squared statistic, which is an intuitive and easily understood concept. The effect of a treatment might also vary across clusters in a cluster randomized trial, or across centres in multi-centre randomized trial, and it can be of interest to explore this at the analysis stage. In cross-over trials and other randomized designs, in which clusters or centres are exposed to both treatment and control conditions, this treatment effect heterogeneity can be identified. Here we derive and evaluate a comparable I-squared measure to describe the magnitude of heterogeneity in treatment effects across clusters or centres in randomized trials. We further show how this methodology can be used to estimate treatment effect heterogeneity in an individual patient data meta-analysis.

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

Pharmaceutical Statistics ◽

10.1002/pst.2141 ◽

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

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Sofosbuvir/daclatasvir regimens for the treatment of COVID-19: an individual patient data meta-analysis

Journal of Antimicrobial Chemotherapy ◽

10.1093/jac/dkaa418 ◽

2020 ◽

Author(s):

Bryony Simmons ◽

Hannah Wentzel ◽

Sara Mobarak ◽

Gholamali Eslami ◽

Anahita Sadeghi ◽

...

Keyword(s):

Risk Ratio ◽

Meta Analysis ◽

Patient Data ◽

Individual Level ◽

Clinical Recovery ◽

Individual Trial ◽

Inverse Variance ◽

All Cause Mortality ◽

The Individual

Abstract Background The combination of sofosbuvir and daclatasvir has a well-established safety profile and improves clinical outcomes in HCV patients. In silico and in vitro studies suggest that sofosbuvir/daclatasvir may show antiviral activity against SARS-CoV-2. Methods Three clinical trials comparing sofosbuvir/daclatasvir-based regimens with a comparator in hospitalized COVID-19 patients were combined in a meta-analysis. The primary outcomes measured were clinical recovery within 14 days of randomization, time to clinical recovery and all-cause mortality. A two-step approach was used to analyse individual-level patient data. The individual trial statistics were pooled using the random-effects inverse-variance model. Results Our search identified eight studies of which three met the inclusion criteria (n = 176 patients); two studies were randomized and one was non-randomized. Baseline characteristics were similar across treatment arms. Clinical recovery within 14 days of randomization was higher in the sofosbuvir/daclatasvir arms compared with control arms [risk ratio = 1.34 (95% CI = 1.05–1.71), P = 0.020]. Sofosbuvir/daclatasvir improves time to clinical recovery [HR = 2.04 (95% CI = 1.25–3.32), P = 0.004]. The pooled risk of all-cause mortality was significantly lower in the sofosbuvir/daclatasvir arms compared with control arms [risk ratio = 0.31 (95% CI = 0.12–0.78), P = 0.013]. Conclusions Available evidence suggests that sofosbuvir/daclatasvir improves survival and clinical recovery in patients with moderate to severe COVID-19. However, the sample size for analysis was relatively small, one of the trials was not randomized and the designs were not standardized. These results need to be confirmed in larger randomized controlled trials.

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