One-step validation method for surrogate endpoints using data from multiple randomized cancer clinical trials with failure-time endpoints

Time-to-event surrogate endpoint validation using mediation analysis and meta-analytic data

With the ongoing development of treatments and the resulting increase in survival in oncology, clinical trials based on endpoints such as overall survival may require long follow-up periods to observe sufficient events and ensure adequate statistical power. This increase in follow-up time may compromise the feasibility of the study. The use of surrogate endpoints instead of final endpoints may be attractive for these studies. However, before a surrogate can be used in a clinical trial, it must be statistically validated. In this article, we propose an approach to validate surrogates when both the surrogate and final endpoints are censored event times. This approach is developed for meta-analytic data and uses a mediation analysis to decompose the total effect of the treatment on the final endpoint as a direct effect and an indirect effect through the surrogate. The meta-analytic nature of the data is accounted for in a joint model with random effects at the trial level. The proportion of the indirect effect over the total effect of the treatment on the final endpoint can be computed from the parameters of the model and used as a measure of surrogacy. We applied this method to investigate time-to-relapse as a surrogate endpoint for overall survival in resectable gastric cancer.

Survival outcomes of patients with metastatic non-small cell lung cancer receiving chemotherapy or immunotherapy as first-line in a real-life setting

Treatment of metastatic non-small cell lung cancer (mNSCLC) has been modified due to the development of immunotherapy. We assessed survival outcomes (overall [OS] and progression-free [rwPFS] survivals, time-to-next-treatment [TNT]) in mNSCLC patients after first-line immunotherapy and chemotherapy in real-life settings. Association between rwPFS and TNT, two candidate surrogate endpoints (SE), with OS was assessed. This retrospective multi-center study uses data from patients included in the Epidemio-Strategy Medico-Economic program with mNSCLC over 2015-2019. The impact of treatment on rwPFS/OS was evaluated with Cox models. Individual-level associations between SE and OS were estimated with an iterative multiple imputation approach and joint survival models. The population included 5294 patients (63 years median age). Median OS in immunotherapy group was 16.4 months (95%CI [14.1-NR]) and was higher than in chemotherapy group (11.6 months; 95%CI [11.0-12.2]). Improved OS was observed for the immunotherapy group after 3 months for subjects with performance status 0-1 (HR = 0.59; 95%CI [0.42-0.83], p < 0.01). The associations between rwPFS and TNT with OS were close ([Formula: see text]=0.57). Results emphasized a survival improvement with immunotherapy for patients in good health condition. There was moderate evidence of individual-level association between candidate SE and OS.

Time to next treatment or death as a candidate surrogate endpoint for overall survival in advanced melanoma patients treated with immune checkpoint inhibitors: an insight from the phase III CheckMate 067 trial

Background

Time to next treatment or death (TNT-D) may be a patient-relevant endpoint in patients treated with immune checkpoint inhibitors. This study investigated TNT-D as a surrogate endpoint (SE) for overall survival (OS) in previously untreated advanced melanoma patients.

Methods

Patient-level data from the 60-month results of the CheckMate 067 randomised, controlled trial were used. Analyses were carried out for nivolumab monotherapy or nivolumab with ipilimumab versus ipilimumab monotherapy. The SE 1-step validation method based on a joint frailty-copula model was used where the country of enrolment was applied to define clusters. Kendall’s τ and the coefficient of determination (R²_trial) were estimated for respective measurements of association at the individual and cluster levels. The surrogate threshold effect, the maximum threshold hazard ratio for TNT-D that would translate into OS benefit, was estimated. A leave-one-out cross-validation analysis was carried out to evaluate model robustness.

Results

Fifteen clusters of data were generated from 945 patients. For both nivolumab-containing arms, the association between TNT-D and OS was deemed acceptable at the individual level (Kendall’s τ > 0.60) and strong at the cluster level, with R²_trial fairly close to 1, with narrow confidence intervals. The estimated surrogate threshold effects were 0.61 for nivolumab versus ipilimumab and 0.49 for nivolimub + ipilimumab versus ipilimumab. Cross-validation results showed minimum variation of the correlation measures and satisfactory predictive accuracy for the model.

Conclusion

Results suggest that TNT-D may be a valuable SE in previously untreated advanced melanoma patients treated with immune checkpoint inhibitors. Surrogacy analyses considering multiple randomised controlled trials are warranted for confirming these findings.

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This is the first study to assess the surrogacy properties of TNT-D for OS in immune checkpoint inhibitor-treated patients.

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TNT-D is a clinically relevant, pragmatic and often measurable endpoint that reflects the result of a therapeutic decision.

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TNT-D appears to be a promising SE for OS in advanced melanoma patients treated with immune checkpoint inhibitors.

Conditional copula models for correlated survival endpoints: Individual patient data meta-analysis of randomized controlled trials

Correlations among survival endpoints are important for exploring surrogate endpoints of the true endpoint. With a valid surrogate endpoint tightly correlated with the true endpoint, the efficacy of a new drug/treatment can be measurable on it. However, the existing methods for measuring correlation between two endpoints impose an invalid assumption: correlation structure is constant across different treatment arms. In this article, we reconsider the definition of Kendall's concordance measure (tau) in the context of individual patient data meta-analyses of randomized controlled trials. According to our new definition of Kendall's tau, its value depends on the treatment arms. We then suggest extending the existing copula (and frailty) models so that their Kendall's tau can vary across treatment arms. Our newly proposed model, a joint frailty-conditional copula model, is the implementation of the new definition of Kendall's tau in meta-analyses. In order to facilitate our approach, we develop an original R function condCox.reg(.) and make it available in the R package joint.Cox (https://CRAN.R-project.org/package=joint.Cox). We apply the proposed method to a gastric cancer dataset (3288 patients in 14 randomized trials from the GASTRIC group). This data analysis concludes that Kendall's tau has different values between the surgical treatment arm and the adjuvant chemotherapy arm (p-value<0.001), whereas disease-free survival remains a valid surrogate at individual level for overall survival in these trials.

A joint frailty-copula model for meta-analytic validation of failure time surrogate endpoints in clinical trials

In a meta-analysis framework, the classical approach for the validation of time-to-event surrogate endpoint is based on a two-step analysis. This approach often raises estimation issues. Recently, we proposed a one-step validation approach based on a joint frailty model. This approach was quite time consuming, despite parallel computing, due to individual-level frailties used to take into account heterogeneity in the data at the individual level. We now propose an alternative one-step approach for evaluating surrogacy, using a joint frailty-copula model. The model includes two correlated random effects treatment-by-trial interaction and a shared random effect associated with the baseline risks. At the individual level, the joint survivor functions of time-to-event endpoints are linked using copula functions. We used splines for the baseline hazard functions. We estimated parameters and hazard function using a semiparametric penalized marginal likelihood method, considering various numerical integration methods. Both individual-level and trial-level surrogacy were evaluated using Kendall's tau and coefficient of determination. The performance of the estimators was evaluated using simulation studies. The model was applied to individual patient data meta-analyses in advanced ovarian cancer to assess progression-free survival as a surrogate for overall survival, as part of the evaluation of new therapy. The model showed good performance and was quite robust regarding the integration methods and data variation, regardless of the surrogacy evaluation criteria. Kendall's Tau was better estimated using the Clayton copula model compared to the joint frailty model. The proposed model reduces the convergence and model estimation issues encountered in the two-step approach.

How to use frailtypack for validating failure-time surrogate endpoints using individual patient data from meta-analyses of randomized controlled trials

Background and Objective

The use of valid surrogate endpoints can accelerate the development of phase III trials. Numerous validation methods have been proposed with the most popular used in a context of meta-analyses, based on a two-step analysis strategy. For two failure time endpoints, two association measures are usually considered, Kendall’s τ at individual level and adjusted R2 (adjRtrial2) at trial level. However, adjRtrial2 is not always available mainly due to model estimation constraints. More recently, we proposed a one-step validation method based on a joint frailty model, with the aim of reducing estimation issues and estimation bias on the surrogacy evaluation criteria. The model was quite robust with satisfactory results obtained in simulation studies. This study seeks to popularize this new surrogate endpoints validation approach by making the method available in a user-friendly R package.

Methods

We provide numerous tools in the frailtypack R package, including more flexible functions, for the validation of candidate surrogate endpoints using data from multiple randomized clinical trials.

Results

We implemented the surrogate threshold effect which is used in combination with Rtrial2 to make decisions concerning the validity of the surrogate endpoints. It is also possible thanks to frailtypack to predict the treatment effect on the true endpoint in a new trial using the treatment effect observed on the surrogate endpoint. The leave-one-out cross-validation is available for assessing the accuracy of the prediction using the joint surrogate model. Other tools include data generation, simulation study and graphic representations. We illustrate the use of the new functions with both real data and simulated data.

Conclusion

This article proposes new attractive and well developed tools for validating failure time surrogate endpoints.