Following recommendations from the feasibility assessment, the Bayesian NMA included scenario analyses to explore the potential impact of suspected effect modifiers from clinical heterogeneity across trial populations and within subgroups. For example, we conducted subgroup analyses for TNBC and HR-positive/HER2-negative patients. Subgroup analyses by line of therapy were also planned for patients receiving treatment in the 2 L+ versus 3 L+ setting, but ultimately not conducted due to a lack of data allowing for this stratification, as discussed further below (see “Results”).

In each analysis, estimates for ERI versus each comparator of interest were obtained using long-established Bayesian NMA techniques [10, 11]. A fixed-effect model was used as the primary analysis approach for each outcome due to limited heterogeneity between trials; however, if two or more studies were available for at least one treatment comparison that showed some evidence of heterogeneity (i.e., when pairwise I2 ≥ 25%, where I2 is the percentage of variation in the treatment estimates between studies beyond change), a random-effect model was employed.

All Bayesian analyses were conducted in OpenBUGS 3.2.3, with 100,000 iterations for “burn-in” and 100,000 iterations for the posterior inference. Each NMA provided an estimate of the relative treatment effect (e.g., a hazard ratio [HR] for OS and PFS or an odds ratio [OR] for SAEs, along with a 95% credible interval [CrI]). Throughout the results, the terminology ‘statistical difference’ or ‘statistically’ are used in instances where CrIs for HRs or ORs do not include 1.

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