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Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process.

ABSTRACT: A number of meta-analytical methods have been proposed that aim to evaluate surrogate endpoints. Bivariate meta-analytical methods can be used to predict the treatment effect for the final outcome from the treatment effect estimate measured on the surrogate endpoint while taking into account the uncertainty around the effect estimate for the surrogate endpoint. In this paper, extensions to multivariate models are developed aiming to include multiple surrogate endpoints with the potential benefit of reducing the uncertainty when making predictions. In this Bayesian multivariate meta-analytic framework, the between-study variability is modelled in a formulation of a product of normal univariate distributions. This formulation is particularly convenient for including multiple surrogate endpoints and flexible for modelling the outcomes which can be surrogate endpoints to the final outcome and potentially to one another. Two models are proposed, first, using an unstructured between-study covariance matrix by assuming the treatment effects on all outcomes are correlated and second, using a structured between-study covariance matrix by assuming treatment effects on some of the outcomes are conditionally independent. While the two models are developed for the summary data on a study level, the individual-level association is taken into account by the use of the Prentice's criteria (obtained from individual patient data) to inform the within study correlations in the models. The modelling techniques are investigated using an example in relapsing remitting multiple sclerosis where the disability worsening is the final outcome, while relapse rate and MRI lesions are potential surrogates to the disability progression.

SUBMITTER: Bujkiewicz S

PROVIDER: S-EPMC4950070 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process.

Bujkiewicz Sylwia S Thompson John R JR Riley Richard D RD Abrams Keith R KR

Statistics in medicine 20151103 7

A number of meta-analytical methods have been proposed that aim to evaluate surrogate endpoints. Bivariate meta-analytical methods can be used to predict the treatment effect for the final outcome from the treatment effect estimate measured on the surrogate endpoint while taking into account the uncertainty around the effect estimate for the surrogate endpoint. In this paper, extensions to multivariate models are developed aiming to include multiple surrogate endpoints with the potential benefit ...[more]

PMID: 26530518

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Project description:BackgroundRecent phase 3 trials have shown an overall survival benefit in metastatic melanoma. We aimed to assess whether progression-free survival (PFS) could be regarded as a reliable surrogate for overall survival through a meta-analysis of randomised trials.MethodsWe systematically reviewed randomised trials comparing treatment regimens in metastatic melanoma that included dacarbazine as the control arm, and which reported both PFS and overall survival with a standard hazard ratio (HR). We correlated HRs for overall survival and PFS, weighted by sample size or by precision of the HR estimate, assuming fixed and random effects. We did sensitivity analyses according to presence of crossover, trial size, and dacarbazine dose.FindingsAfter screening 1649 reports and meeting abstracts published before Sept 8, 2013, we identified 12 eligible randomised trials that enrolled 4416 patients with metastatic melanoma. Irrespective of weighting strategy, we noted a strong correlation between the treatment effects for PFS and overall survival, which seemed independent of treatment type. Pearson correlation coefficients were 0·71 (95% CI 0·29-0·90) with a random-effects assumption, 0·85 (0·59-0·95) with a fixed-effects assumption, and 0·89 (0·68-0·97) with sample-size weighting. For nine trials without crossover, the correlation coefficient was 0·96 (0·81-0·99), which decreased to 0·93 (0·74-0·98) when two additional trials with less than 50% crossover were included. Inclusion of mature follow-up data after at least 50% crossover (in vemurafenib and dabrafenib phase 3 trials) weakened the PFS to overall survival correlation (0·55, 0·03-0·84). Inclusion of trials with no or little crossover with the random-effects assumption yielded a conservative statement of the PFS to overall survival correlation of 0·85 (0·51-0·96).InterpretationPFS can be regarded as a robust surrogate for overall survival in dacarbazine-controlled randomised trials of metastatic melanoma; we postulate that this association will hold as treatment standards evolve and are adopted as the control arm in future trials.FundingNone.

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Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process.

Publications

Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process.

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