Dataset Information

Bivariate network meta-analysis for surrogate endpoint evaluation.

ABSTRACT: Surrogate endpoints are very important in regulatory decision making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and act as good predictors of clinical benefit. Bivariate meta-analysis methods can be used to evaluate surrogate endpoints and to predict the treatment effect on the final outcome from the treatment effect measured on a surrogate endpoint. However, candidate surrogate endpoints are often imperfect, and the level of association between the treatment effects on the surrogate and final outcomes may vary between treatments. This imposes a limitation on methods which do not differentiate between the treatments. We develop bivariate network meta-analysis (bvNMA) methods, which combine data on treatment effects on the surrogate and final outcomes, from trials investigating multiple treatment contrasts. The bvNMA methods estimate the effects on both outcomes for all treatment contrasts individually in a single analysis. At the same time, they allow us to model the trial-level surrogacy patterns within each treatment contrast and treatment-level surrogacy, thus enabling predictions of the treatment effect on the final outcome either for a new study in a new population or for a new treatment. Modelling assumptions about the between-studies heterogeneity and the network consistency, and their impact on predictions, are investigated using an illustrative example in advanced colorectal cancer and in a simulation study. When the strength of the surrogate relationships varies across treatment contrasts, bvNMA has the advantage of identifying treatment comparisons for which surrogacy holds, thus leading to better predictions.

SUBMITTER: Bujkiewicz S

PROVIDER: S-EPMC6618064 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Bivariate network meta-analysis for surrogate endpoint evaluation.

Bujkiewicz Sylwia S Jackson Dan D Thompson John R JR Turner Rebecca M RM Städler Nicolas N Abrams Keith R KR White Ian R IR

Statistics in medicine 20190526 18

Surrogate endpoints are very important in regulatory decision making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and act as good predictors of clinical benefit. Bivariate meta-analysis methods can be used to evaluate surrogate endpoints and to predict the treatment effect on the final outcome from the treatment effect measured on a surrogate endpoint. However, candidate surrogate endpoints are often imperfect, and the level of assoc ...[more]

PMID: 31131475

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Project description:ObjectiveTo evaluate pathological complete response as a surrogate endpoint for disease-free survival and overall survival in regulatory neoadjuvant trials of early stage breast cancer.DesignSystematic review and meta-analysis.Data sourcesMedline, Embase, and Scopus to 1 December 2020.Eligibility criteria for study selectionRandomised clinical trials that tested neoadjuvant chemotherapy given alone or combined with other treatments, including anti-human epidermal growth factor 2 (anti-HER2) drugs, targeted treatments, antivascular agents, bisphosphonates, and immune checkpoint inhibitors.Data extraction and synthesisTrial level associations between the surrogate endpoint pathological complete response and disease-free survival and overall survival.MethodsA weighted regression analysis was performed on log transformed treatment effect estimates (hazard ratio for disease-free survival and overall survival and relative risk for pathological complete response), and the coefficient of determination (R2) was used to quantify the association. The secondary objective was to explore heterogeneity of results in preplanned subgroups analysis, stratifying trials according treatment type in the experimental arm, definition used for pathological complete response (breast and lymph nodes v breast only), and biological features of the disease (HER2 positive or triple negative breast cancer). The surrogate threshold effect was also evaluated, indicating the minimum value of the relative risk for pathological complete response necessary to confidently predict a non-null effect on hazard ratio for disease-free survival or overall survival.Results54 randomised clinical trials comprising a total of 32 611 patients were included in the analysis. A weak association was observed between the log(relative risk) for pathological complete response and log(hazard ratio) for both disease-free survival (R2=0.14, 95% confidence interval 0.00 to 0.29) and overall survival (R2 =0.08, 0.00 to 0.22). Similar results were found across all subgroups evaluated, independently of the definition used for pathological complete response, treatment type in the experimental arm, and biological features of the disease. The surrogate threshold effect was 5.19 for disease-free survival but was not estimable for overall survival. Consistent results were confirmed in three sensitivity analyses: excluding small trials (<200 patients enrolled), excluding trials with short median follow-up (<24 months), and replacing the relative risk for pathological complete response with the absolute difference of pathological complete response rates between treatment arms.ConclusionA lack of surrogacy of pathological complete response was identified at trial level for both disease-free survival and overall survival. The findings suggest that pathological complete response should not be used as primary endpoint in regulatory neoadjuvant trials of early stage breast cancer.

Project description: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 [Formula: see text] 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.

Project description:BACKGROUND: When multiple endpoints are of interest in evidence synthesis, a multivariate meta-analysis can jointly synthesise those endpoints and utilise their correlation. A multivariate random-effects meta-analysis must incorporate and estimate the between-study correlation (rhoB). METHODS: In this paper we assess maximum likelihood estimation of a general normal model and a generalised model for bivariate random-effects meta-analysis (BRMA). We consider two applied examples, one involving a diagnostic marker and the other a surrogate outcome. These motivate a simulation study where estimation properties from BRMA are compared with those from two separate univariate random-effects meta-analyses (URMAs), the traditional approach. RESULTS: The normal BRMA model estimates rhoB as -1 in both applied examples. Analytically we show this is due to the maximum likelihood estimator sensibly truncating the between-study covariance matrix on the boundary of its parameter space. Our simulations reveal this commonly occurs when the number of studies is small or the within-study variation is relatively large; it also causes upwardly biased between-study variance estimates, which are inflated to compensate for the restriction on rhoB. Importantly, this does not induce any systematic bias in the pooled estimates and produces conservative standard errors and mean-square errors. Furthermore, the normal BRMA is preferable to two normal URMAs; the mean-square error and standard error of pooled estimates is generally smaller in the BRMA, especially given data missing at random. For meta-analysis of proportions we then show that a generalised BRMA model is better still. This correctly uses a binomial rather than normal distribution, and produces better estimates than the normal BRMA and also two generalised URMAs; however the model may sometimes not converge due to difficulties estimating rhoB. CONCLUSION: A BRMA model offers numerous advantages over separate univariate synthesises; this paper highlights some of these benefits in both a normal and generalised modelling framework, and examines the estimation of between-study correlation to aid practitioners.

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Bivariate network meta-analysis for surrogate endpoint evaluation.

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Bivariate network meta-analysis for surrogate endpoint evaluation.

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