Validation Of Surrogate Endpoints Research Articles

Using Bayesian Evidence Synthesis Methods to Incorporate Real-World Evidence in Surrogate Endpoint Evaluation.

Traditionally, validation of surrogate endpoints has been carried out using randomized controlled trial (RCT) data. However, RCT data may be too limited to validate surrogate endpoints. In this article, we sought to improve the validation of surrogate endpoints with the inclusion of real-world evidence (RWE). We use data from comparative RWE (cRWE) and single-arm RWE (sRWE) to supplement RCT evidence for the evaluation of progression-free survival (PFS) as a surrogate endpoint to overall survival (OS) in metastatic colorectal cancer (mCRC). Treatment effect estimates from RCTs, cRWE, and matched sRWE, comparing antiangiogenic treatments with chemotherapy, were used to inform surrogacy patterns and predictions of the treatment effect on OS from the treatment effect on PFS. Seven RCTs, 4 cRWE studies, and 2 matched sRWE studies were identified. The addition of RWE to RCTs reduced the uncertainty around the estimates of the parameters for the surrogate relationship. The addition of RWE to RCTs also improved the accuracy and precision of predictions of the treatment effect on OS obtained using data on the observed effect on PFS. The addition of RWE to RCT data improved the precision of the parameters describing the surrogate relationship between treatment effects on PFS and OS and the predicted clinical benefit of antiangiogenic therapies in mCRC. Regulatory agencies increasingly rely on surrogate endpoints when making licensing decisions, and for the decisions to be robust, surrogate endpoints need to be validated. In the era of precision medicine, when surrogacy patterns may depend on the drug's mechanism of action and trials of targeted therapies may be small, data from randomized controlled trials may be limited.Real-world evidence (RWE) is increasingly used at different stages of the drug development process. When used to enhance the evidence base for surrogate endpoint evaluation, RWE can improve inferences about the strength of surrogate relationships and the precision of predicted treatment effect on the final clinical outcome based on the observed effect on the surrogate endpoint in a new trial.Careful selection of RWE is needed to reduce risk of bias.

FDA validation of surrogate endpoints in oncology: 2005–2022

IntroductionThe number of oncologic drugs approved by the US Food and Drug Administration (FDA) on the basis of surrogate endpoints is rising. However, many surrogates have not demonstrated a correlation with clinically meaningful outcomes like overall survival. We sought to investigate surrogate validation studies conducted by the FDA over the past 17 years. MethodsWe reviewed analyses of surrogate outcomes published by the FDA from 2005 to 2022. Data extracted included the number of clinical trials included in each analysis, the associations of surrogate outcomes with OS or other surrogates, and the authors’ interpretation of these associations. ResultsOf the 15 surrogate analyses conducted by the FDA, only one demonstrated a strong correlation between a surrogate outcome and overall survival. 87% only included clinical trials submitted to the FDA in their analysis, and all were published from 2014 onwards. DiscussionThe vast majority of FDA analyses of surrogate outcomes did not find strong correlations between surrogates and overall survival, raising concern about the use of such outcomes as endpoints in clinical trials. As most studies were based on limited data, further research is required to assess the true validity of surrogate outcomes. Policy summaryDrugs approved on the basis of surrogates that are not associated with clinically meaningful outcomes can cause significant harm to patients. Until surrogate outcomes have been thoroughly and robustly validated, they should be used with caution in drug approval decisions.

Clinical Trial Considerations in Developing Treatments for Early Stages of Common, Chronic Kidney Diseases: A Scientific Workshop Cosponsored by the National Kidney Foundation and the US Food and Drug Administration

In the past decade, advances in the validation of surrogate end points for chronic kidney disease (CKD) progression have heightened interest in evaluating therapies in early CKD. In December 2020, the National Kidney Foundation sponsored a scientific workshop in collaboration with the US Food and Drug Administration (FDA) to explore patient, provider, and payor perceptions of the value of treating early CKD. The workshop reviewed challenges for trials in early CKD, including trial designs, identification of high-risk populations, and cost-benefit and safety considerations. Over 90 people representing a range of stakeholders including experts in clinical trials, nephrology, cardiology and endocrinology, patient advocacy organizations, patients, payors, health economists, regulators and policy makers attended a virtual meeting. There was consensus among the attendees that there is value to preventing the development and treating the progression of early CKD in people who are at high risk for progression, and that surrogate end points should be used to establish efficacy. Attendees also concluded that cost analyses should be holistic and include aspects beyond direct savings for treatment of kidney failure; and that safety data should be collected outside/beyond the duration of a clinical trial. Successful drug development and implementation of effective therapies will require collaboration across sponsors, patients, patient advocacy organizations, medical community, regulators, and payors.

Use of copula to model within-study association in bivariate meta-analysis of binomial data at the aggregate level: ABayesian approach and application to surrogate endpoint evaluation.

Bivariate meta-analysis provides a useful framework for combining information across related studies and has been utilized to combine evidence from clinical studies to evaluate treatment efficacy on two outcomes. It has also been used to investigate surrogacy patterns between treatment effects on the surrogate endpoint and the final outcome. Surrogate endpoints play an important role in drug development when they can be used to measure treatment effect early compared to the final outcome and to predict clinical benefit or harm. The standard bivariate meta-analytic approach models the observed treatment effects on the surrogate and the final outcome outcomes jointly, at both the within-study and between-studies levels, using a bivariate normal distribution. For binomial data, a normal approximation on log odds ratio scale can be used. However, this method may lead to biased results when the proportions of events are close to one or zero, affecting the validation of surrogate endpoints. In this article, we explore modeling the two outcomes on the original binomial scale. First, we present a method that uses independent binomial likelihoods to model the within-study variability avoiding to approximate the observed treatment effects. However, the method ignores the within-study association. To overcome this issue, we propose a method using a bivariate copula with binomial marginals, which allows the model to account for the within-study association. We applied the methods to an illustrative example in chronic myeloid leukemia to investigate the surrogate relationship between complete cytogenetic response and event-free-survival.

Accelerated orphan drug approval: surrogate endpoints

Today, orphan drug development is confronted with significant challenges represented by the considerable complexity, diversity of clinical manifestations, and competition in study recruitment. Thus, surrogate endpoints adoption plays a crucial role in rare disease trials by minimizing costs, the number of subjects, and study duration. Surrogate endpoints, to substitute for a direct measure of how patients feel, function, or survive, must be biomarkers that directly correlate with disease clinical manifestations and predict the impact of study drug on the long-term disease progression. Validation of surrogate endpoints for accuracy and sensitivity is essential to maximize its benefit and utility. These validation criteria include reliability, reproducibility, keenness, and a direct reflection of patient feeling, function, or survival upon treatment. On the other hand, the selection of surrogate endpoints may be pretty complex, and making mistakes may lead to inaccurate estimates of the clinical benefit. Finally, surrogate endpoints contribute to a composite endpoint when studying drug benefits patients in multiple ways, and not all the measured components are detected in each patient. Furthermore, a composite endpoint comprised of multiple surrogate endpoints could improve statistical efficiency. Selection and qualification of biomarkers for surrogate endpoints and accelerated market approval is often a complex process that requires experience and method. Today, we contributed to developing orphan drugs for many rare diseases, including Neurological Diseases, Lysosomal Storage Diseases, Metabolic Conditions, Immune Disorders, and Cancers. In conclusion, surrogate endpoints play an essential role in orphan drug development, benefiting patients and the healthcare system.

Biomarkers for drug development in propionic and methylmalonic acidemias.

There is an unmet need for the development and validation of biomarkers and surrogate endpoints for clinical trials in propionic acidemia (PA) and methylmalonic acidemia (MMA). This review examines the pathophysiology and clinical consequences of PA and MMA that could form the basis for potential biomarkers and surrogate endpoints. Changes in primary metabolites such as methylcitric acid (MCA), MCA:citric acid ratio, oxidation of 13C‐propionate (exhaled 13CO2), and propionylcarnitine (C3) have demonstrated clinical relevance in patients with PA or MMA. Methylmalonic acid, another primary metabolite, is a potential biomarker, but only in patients with MMA. Other potential biomarkers in patients with either PA and MMA include secondary metabolites, such as ammonium, or the mitochondrial disease marker, fibroblast growth factor 21. Additional research is needed to validate these biomarkers as surrogate endpoints, and to determine whether other metabolites or markers of organ damage could also be useful biomarkers for clinical trials of investigational drug treatments in patients with PA or MMA. This review examines the evidence supporting a variety of possible biomarkers for drug development in propionic and methylmalonic acidemias.

Significance of emerging clinical oncology endpoints in support of overall survival.

Despite a better understanding of the pathophysiology and development of newer therapeutic options, cancer remains an area with several unmet needs. Although overall survival (OS) remains a gold standard endpoint for all cancer therapies, it poses challenges such as the requirement of a long-term follow-up, a higher number of patients, and a higher financial burden. Therefore, surrogate endpoints such as progression-free survival, time to progression, duration of response, and objective response rate are being investigated and used in oncology studies. Patient-related outcomes that measure the patient's overall health, quality of life, and satisfaction in the long term are crucial surrogate endpoints considered for drug approval. Surrogate endpoints shorten oncology clinical studies and accelerate the evaluation and implementation of newer therapies. Emerging surrogate endpoints such as biomarkers, immune-related response criteria, minimal residual disease, and pathological complete response are increasingly being considered in oncology trials. Validation of surrogate endpoints enables their substitution for OS and gain market approval. The selection of surrogate endpoints for an oncology trial depends on cancer type and stage, the purpose of treatment, and expected duration of survival for the relevant disease. With the advent of individualized approach and complex study designs, the field of oncology is currently undergoing a paradigm shift. The use of newer surrogate endpoints will aid in accelerating the drug development process, making patient care for oncology more accessible.

PCN24 Evaluating the Surrogacy of Objective Response Rate for Overall Survival in Oncology Clinical Trials: A Methodologic Review

Overall survival (OS) is considered the gold-standard primary endpoint in oncology clinical trials. Protective OS treatment effects demonstrate clear patient benefits; however, large sample sizes and extensive follow-up are required, potentially delaying access to novel therapeutics. The use of intermediate endpoints such as objective response rate (ORR) as surrogates for OS offers a solution to these challenges but requires validation. A variety of techniques are available to conduct surrogate endpoint validation. We sought to compare the approaches used to assess the surrogacy of ORR for OS in oncology clinical trials. The PubMed and Cochrane Systematic Reviews databases were queried from 2010 to 2020 to identify studies that assessed ORR as a surrogate for OS. Analytical strategies were reviewed and extracted to compare OS endpoints (median, hazard ratio [HR]), ORR endpoints (percentage, odds ratio [OR], relative risk [RR]), correlation types, regression models, surrogate threshold effects (STE), and controlling for patient crossover. The review identified 24 meta-analysis studies in 12 solid tumor and hematologic oncology populations. Median OS and OS HR were the primary endpoints in 9/24 (37.5%) and 17/24 (70.8%), respectively. Choice of ORR measure varied among studies; absolute ORR was used in 9/24 (37.5%), ORR OR in 15/24 (62.5%), and ORR RR in 6/24 (25.0%), with several studies employing multiple analyses. Furthermore, these measures were logarithmically transformed in 11/24 (45.8%) studies. Correlations were estimated in 15/24 (62.5%), and associations were modeled using regression techniques in 23/24 (95.8%). Few studies estimated the STE (20.8%) or attempted to control for patient crossover (16.7%). Methodologies to validate ORR as a surrogate endpoint for OS varied between studies. Further research is required to understand the impact of analytical methodology on the validation of surrogate endpoints, which may inform best practices for future studies.

Surrogate endpoint evaluation using data from one large global randomized controlled trial

BackgroundRobust identification of surrogate endpoints can help accelerate the development of pharmacotherapies for diseases traditionally evaluated using true endpoints associated with prolonged follow-up. The meta-analysis-based surrogate endpoint evaluation (SEE) integrates data from multiple, usually smaller, trials to statistically confirm a surrogate endpoint as a robust proxy for the true endpoint. To test the applicability of SEE when only a single, larger trial is available, we analysed the cardiovascular (CV) survival endpoint from the large multinational trial LEADER (9340 subjects) that confirmed the CV safety of a diabetes drug (liraglutide). We evaluated if using country as a trial unit adequately facilitated the meta-analysis and calculation of R2 by country group.MethodsData were grouped by country, ensuring at least 30 CV deaths (497 in total) in each of the nine resulting by-country groups. In a two-step SEE on the grouped dataset, we first fitted the group-specific Cox proportional hazard models; next, on the trial-level, we regressed the estimated hazard ratio (HR; liraglutide vs placebo) of the true endpoints (CV death: 497 events, or all-cause death: 828 events) on the HR of the surrogate endpoint (major CV adverse event [MACE]: 1302 events) and derived the group-specific R2 and its 95% confidence interval (CI).ResultsGroup-level surrogacy of MACE was supported for CV death but not for all-cause death, with {text{R}}_{{{text{group}}}}^{2} values of 0.85 [0.63;1.00]95% CI and 0.23 [0.00;0.67]95% CI, respectively. Sensitivity analyses using different grouping approaches (e.g. grouping by region) corroborated the robustness of the conclusions as well as the appropriateness of the data-grouping approaches.ConclusionsWe derived a specific grouping approach to successfully apply SEE on data from a single trial. This may allow for the statistically robust identification and validation of surrogate endpoints based on the abundance of large monolithic outcome trials conducted as part of drug development programmes in, for example, diabetes.

Validity of Surrogate Endpoints and Their Impact on Coverage Recommendations: A Retrospective Analysis across International Health Technology Assessment Agencies.

BackgroundSurrogate endpoints (i.e., intermediate endpoints intended to predict for patient-centered outcomes) are increasingly common. However, little is known about how surrogate evidence is handled in the context of health technology assessment (HTA).Objectives1) To map methodologies for the validation of surrogate endpoints and 2) to determine their impact on acceptability of surrogates and coverage decisions made by HTA agencies.MethodsWe sought HTA reports where evaluation relied on a surrogate from 8 HTA agencies. We extracted data on the methods applied for surrogate validation. We assessed the level of agreement between agencies and fitted mixed-effects logistic regression models to test the impact of validation approaches on the agency’s acceptability of the surrogate endpoint and their coverage recommendation.ResultsOf the 124 included reports, 61 (49%) discussed the level of evidence to support the relationship between the surrogate and the patient-centered endpoint, 27 (22%) reported a correlation coefficient/association measure, and 40 (32%) quantified the expected effect on the patient-centered outcome. Overall, the surrogate endpoint was deemed acceptable in 49 (40%) reports (k-coefficient 0.10, P = 0.004). Any consideration of the level of evidence was associated with accepting the surrogate endpoint as valid (odds ratio [OR], 4.60; 95% confidence interval [CI], 1.60–13.18, P = 0.005). However, we did not find strong evidence of an association between accepting the surrogate endpoint and agency coverage recommendation (OR, 0.71; 95% CI, 0.23–2.20; P = 0.55).ConclusionsHandling of surrogate endpoint evidence in reports varied greatly across HTA agencies, with inconsistent consideration of the level of evidence and statistical validation. Our findings call for careful reconsideration of the issue of surrogacy and the need for harmonization of practices across international HTA agencies.

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 randomised controlled trials

Introduction 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 tau at individual level and adjusted R2 (adjR2trial) at trial level. However, adjR2trial 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 and a joint frailty-copula 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 popularise this new surrogate endpoints validation approach by making the methods 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 randomised clinical trials. Results We implemented the surrogate threshold effect which is used in combination with R2trial 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 models. 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 work proposes new attractive and well developed tools for validating failure time surrogate endpoints.

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

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 ([Formula: see text]) at trial level. However, [Formula: see text] 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. 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. We implemented the surrogate threshold effect which is used in combination with [Formula: see text] 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. This article proposes new attractive and well developed tools for validating failure time surrogate endpoints.

PNS205 SURROGATE VALIDATION IN GERMAN AMNOG BENEFIT ASSESSMENTS

In clinical trials surrogate endpoints are oftentimes used as substitute for patient-relevant endpoints. However, in the early benefit assessment of pharmaceuticals in Germany and in order be able to provide patient-relevant evidence a validation of surrogate endpoints is critical. The aim of this study was to assess the utilization, methodology and acceptance of surrogate validations in AMNOG benefit dossiers. Dossiers for benefit assessment were retrieved from the homepage of the Federal Joint Committee (G-BA). It was assessed whether the pharmaceutical companies performed a surrogate validation and if a surrogate parameter was considered for benefit assessment by the Institute for Quality and Efficiency in Health Care (IQWiG) and later accepted by the G-BA. In total 397 AMNOG dossiers were analyzed. In 105 (26.4%) dossiers a surrogate validation was presented. Of all attempted surrogate validations, 36 (34.3%) were rejected and 51 (48.6%) were not further commented (e. g. due to limitations in study design). In total, 18 validations (17.1% of all attempted validations) were accepted as valid. Validations were only accepted in infectious indications (94.4%) and oncological diseases (5.6%). Accepted endpoints were (sustained) virologic response for reduced risk for hepatocellular carcinoma as well as AIDS-defining diseases/death, CD4 cell count for AIDS-defining diseases/death and disease-free survival (DFS) for overall survival. In most cases the pharmaceutical companies referred to prior benefit assessments. Only for DFS a comprehensive surrogate validation including systematic literature search and statistical calculations was performed. Even though the methodological demands by IQWiG are high, in one quarter of submitted dossiers pharmaceutical companies attempted to validate a surrogate parameter as a patient-relevant endpoint. Overall, the acceptance of validations is possible; however, the success rate is small (17%). Most accepted surrogate validations were within infectious diseases and their validation referred to prior benefit assessments.

Oncologic orphan drugs approved in the EU \u2013 do clinical trial data correspond with real-world effectiveness?

BackgroundEvaluation of evidence for efficacy of orphan medicinal products (OMPs) for rare malignancies may be hampered by the use of tumor measurements instead of clinical endpoints. This may cause efficacy data to not always match effectiveness in the real-world. We investigated whether an efficacy-effectiveness gap exists for oncologic OMPs and aimed to identify which factors contribute to it. Also, the magnitude of the clinical efficacy of oncologic OMPs was evaluated.MethodsWe included all oncologic OMPs authorized in the European Union from 2000 to 2017. Pivotal studies were evaluated by means of the European Society for Medical Oncology - Magnitude of Clinical Benefit Scale (ESMO-MCBS). To estimate real-world effectiveness, a literature search was performed to identify post-marketing studies, of which data on overall survival (OS) were extracted. OS of the new OMP was compared with OS data of standard of care. An OS gain of ≥3 months compared to pre-marketing data was considered clinically relevant.ResultsTwenty OMPs were included, of which 5 were authorized based on OS as a primary endpoint. 10 OMPs had post-marketing data available, of which 40% did not show a clinically relevant OS gain in the real world. All OMPs that were studied with OS as primary endpoint in the pivotal study had a clinically relevant OS gain in the real world. Furthermore, all OMPs that had a high ESMO-MCBS score and post-marketing data available, resulted in a clinically relevant OS gain in the real world.ConclusionsAlthough the sample size is small, our results indicate an efficacy-effectiveness gap for oncologic OMPs exists. Significant changes in PFS do not always lead to an increased OS. The use of PFS may be justified, but validation of surrogate endpoints is needed.

Evaluation of six months sputum culture conversion as a surrogate endpoint in a multidrug resistant-tuberculosis trial

The emergence of multidrug resistant-tuberculosis (MDR-TB), defined as Mycobacterium tuberculosis strains with in vitro resistance to at least isoniazid and rifampicin, has necessitated evaluation and validation of appropriate surrogate endpoints for treatment response in drug trials for MDR-TB. The trial that has demonstrated efficacy of bedaquiline, a diarylquinoline that inhibits mycobacterial ATP synthase, possesses the requisite features to conduct this evaluation. Approval of bedaquiline for use in MDR-TB was based primarily on the results of the controlled C208 Stage II study (ClinicalTrials.gov number, NCT00449644) including 160 patients randomized 1:1 to receive bedaquiline or placebo for 24 weeks when added to an 18–24-month preferred five-drug background regimen. Since randomization in C208 Stage II was preserved until study end, the trial results allow for the investigation of the complex relationship between sustained durable outcome with either Week 8 or Week 24 culture conversion as putative surrogate endpoints. The relationship between Week 120 outcome with Week 8 or Week 24 culture conversion was investigated using a descriptive analysis and with a recently developed statistical methodology for surrogate endpoint evaluation using methods of causal inference.The results demonstrate that sputum culture conversion at 24 weeks is more reliable than sputum culture conversion at 8 weeks when assessing the outcome of adding one new drug to a MDR-TB regimen.

Validation of Surrogate Endpoints for Clinical Trials

Validation of Surrogate Endpoints for Clinical Trials

Validation of Surrogate Endpoints In Melanoma Therapies and Use In HTA

Validation of Surrogate Endpoints In Melanoma Therapies and Use In HTA

A Poisson approach to the validation of failure time surrogate endpoints in individual patient data meta-analyses.

Surrogate endpoints are often used in clinical trials instead of well-established hard endpoints for practical convenience. The meta-analytic approach relies on two measures of surrogacy: one at the individual level and one at the trial level. In the survival data setting, a two-step model based on copulas is commonly used. We present a new approach which employs a bivariate survival model with an individual random effect shared between the two endpoints and correlated treatment-by-trial interactions. We fit this model using auxiliary mixed Poisson models. We study via simulations the operating characteristics of this mixed Poisson approach as compared to the two-step copula approach. We illustrate the application of the methods on two individual patient data meta-analyses in gastric cancer, in the advanced setting (4069 patients from 20 randomized trials) and in the adjuvant setting (3288 patients from 14 randomized trials).

Validation of surrogate endpoints in cancer clinical trials via principal stratification with an application to a prostate cancer trial.

Increasing attention has been focused on the use and validation of surrogate endpoints in cancer clinical trials. Previous literature on validation of surrogate endpoints are classified into four approaches: the proportion explained approach; the indirect effects approach; the meta-analytic approach; and the principal stratification approach. The mainstream in cancer research has seen the application of a meta-analytic approach. However, VanderWeele (2013) showed that all four of these approaches potentially suffer from the surrogate paradox. It was also shown that, if a principal surrogate satisfies additional criteria called one-sided average causal sufficiency, the surrogate cannot exhibit a surrogate paradox. Here, we propose a method for estimating principal effects under a monotonicity assumption. Specifically, we consider cancer clinical trials which compare a binary surrogate endpoint and a time-to-event clinical endpoint under two naturally ordered treatments (e.g. combined therapy vs. monotherapy). Estimation based on a mean score estimating equation will be implemented by the expectation-maximization algorithm. We will also apply the proposed method as well as other surrogacy criteria to evaluate the surrogacy of prostate-specific antigen using data from a phase III advanced prostate cancer trial, clarifying the complementary roles of both the principal stratification and meta-analytic approaches in the evaluation of surrogate endpoints in cancer. Copyright © 2017 John Wiley & Sons, Ltd.