Combining tumour response and progression free survival as surrogate endpoints for overall survival in advanced colorectal cancer

Elia, Eleni G.; Städler, Nicolas; Ciani, Oriana; Taylor, Rod S; Bujkiewicz, Sylwia

doi:10.1016/j.canep.2019.101665

Cited by 5 publications

(13 citation statements)

References 27 publications

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“…Regression equations were reported in fourteen studies for the relationship between ORR and OS; of these, four reported absolute associations 42,52,72,76 and ten reported treatment effect associations. [31][32][33]36,41,46,56,58,67,77 Regression equations were also reported in eight studies for the relationship between ORR and PFS; of these, four reported absolute associations 52,54,72,76 and four reported treatment effect associations. 24,33,67,77 These analyses spanned 10 cancer types.…”

Section: Regression Equationsmentioning

confidence: 99%

A systematic review of meta-analyses assessing the validity of tumour response endpoints as surrogates for progression-free or overall survival in cancer

et al. 2020

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Section: Regression Equationsmentioning

confidence: 99%

A systematic review of meta-analyses assessing the validity of tumour response endpoints as surrogates for progression-free or overall survival in cancer

et al. 2020

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“…Similar findings were presented in a systematic review of twenty individual RCTs in the second-line treatment of mCRC, with moderate (0.73) and poor (0.17) correlations of PFS and ORR with OS, respectively [ 73 ]. However, the surrogacy relationships, which were performed by Bujkiewicz et al in the Bayesian framework [ 62 ], were determined to have the advantage of considering the uncertainty of measurement errors of treatment effect on surrogate endpoints and allowed us to combine both treatments on ORR and PFS in the calculation of HR for OS [ 63 ]. In the present study, we also observed that the association between HR for OS and HR for PFS (R-squared = 0.54, 95% CrI = 0.25–0.76) was not much improved when adding OR for ORR as the second surrogate endpoint by applying the MVRE models (R-squared = 0.53, 95% CrI = 0.22–0.76).…”

Section: Discussionmentioning

confidence: 99%

“…In the current study,

representing log OR on ORR,

representing log HR on PFS, and

representing log HR on OS are assumed to be correlated and normally distributed [ 63 ]:

where

are the true treatment effects,

are the corresponding variances of treatment effects for individual study

and outcome

, and

are within-study correlations among these estimates. The between-study variability is estimated by modeling

in a conditional univariate normal distribution with structured covariance [ 63 ]:

where the variances

are related to the between-study heterogeneity parameters

through the regression coefficients

, which are related to both

and between-study correlations

. Given that HR of OS is positively associated with HR of PFS and negatively associated with OR of ORR, we allocated uniform prior distributions for the between-study correlations, ...…”

Section: Methodsmentioning

confidence: 99%

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Combining Correlated Outcomes and Surrogate Endpoints in a Network Meta-Analysis of Colorectal Cancer Treatments

Hoang

Kim

2020

Cancers

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This study aimed to investigate the efficacy and safety of systemic therapies in the treatment of unresectable advanced or metastatic colorectal cancer. Predicted hazard ratios (HRs) and their 95% credible intervals (CrIs) for overall survival (OS) were calculated from the odds ratio (OR) for the overall response rate and/or HR for progression-free survival using multivariate random effects (MVRE) models. We performed a network meta-analysis (NMA) of 49 articles to compare the efficacy and safety of FOLFOX/FOLFIRI±bevacizumab (Bmab)/cetuximab (Cmab)/panitumumab (Pmab), and FOLFOXIRI/CAPEOX±Bmab. The NMA showed significant OS improvement with FOLFOX, FOLFOX+Cmab, and FOLFIRI+Cmab compared with that of FOLFIRI (HR = 0.84, 95% CrI = 0.73–0.98; HR = 0.76, 95% CrI = 0.62–0.94; HR = 0.80, 95% CrI = 0.66–0.96, respectively), as well as with FOLFOX+Cmab and FOLFIRI+Cmab compared with that of FOLFOXIRI (HR = 0.69, 95% CrI = 0.51–0.94 and HR = 0.73, 95% CrI = 0.54–0.97, respectively). The odds of adverse events grade ≥3 were significantly higher for FOLFOX+Cmab vs. FOLFIRI+Bmab (OR = 2.34, 95% CrI = 1.01–4.66). Higher odds of events were observed for FOLFIRI+Pmab in comparison with FOLFIRI (OR = 2.16, 95% CrI = 1.09–3.84) and FOLFIRI+Bmab (OR = 3.14, 95% CrI = 1.51–5.89). FOLFOX+Cmab and FOLFIRI+Bmab showed high probabilities of being first- and second-line treatments in terms of the efficacy and safety, respectively. The findings of the efficacy and safety comparisons may support the selection of appropriate treatments in clinical practice. PROSPERO registration: CRD42020153640.

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“…The scatter plot in Figure illustrates the association patterns between the treatment effects on the two outcomes for each treatment contrast. The within‐study correlation between the treatment effects on TR and PFS, required to populate the following bivariate meta‐analytic models, was obtained from individual participant data of a RCT comparing Bevacizumab (anti‐VEGF) with chemotherapy vs. chemotherapy alone, as reported by Elia et al with further details included in the Supplementary Materials. This correlation was assumed to apply to all studies.…”

Section: Illustrative Examplementioning

confidence: 99%

Bivariate network meta‐analysis for surrogate endpoint evaluation

Bujkiewicz

Jackson²,

Thompson

et al. 2019

Statistics in Medicine

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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.

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Combining tumour response and progression free survival as surrogate endpoints for overall survival in advanced colorectal cancer

Cited by 5 publications

References 27 publications

A systematic review of meta-analyses assessing the validity of tumour response endpoints as surrogates for progression-free or overall survival in cancer

A systematic review of meta-analyses assessing the validity of tumour response endpoints as surrogates for progression-free or overall survival in cancer

Combining Correlated Outcomes and Surrogate Endpoints in a Network Meta-Analysis of Colorectal Cancer Treatments

Bivariate network meta‐analysis for surrogate endpoint evaluation

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