Search

Introduction: Poly(ADP-ribose) polymerase inhibitors (PARPi) are a novel option to treat patients with metastatic castration-resistant prostate cancer (mCRPC). Niraparib plus abiraterone acetate and prednisone (AAP) is indicated for BRCA1/2 mutation-positive mCRPC. Niraparib plus AAP demonstrated safety and efficacy in the phase 3 MAGNITUDE trial (NCT03748641). In the absence of head-to-head studies comparing PARPi regimens, the feasibility of conducting indirect treatment comparisons (ITC) to inform decisions for patients with first-line BRCA1/2 mutation-positive mCRPC has been explored., Methods: A systematic literature review was conducted to identify evidence from randomized controlled trials on relevant comparators to inform the feasibility of conducting ITCs via network meta-analysis (NMA) or population-adjusted indirect comparisons (PAIC). Feasibility was assessed based on network connectivity, data availability in the BRCA1/2 mutation-positive population, and degree of within- and between-study heterogeneity or bias., Results: NMAs between niraparib plus AAP and other PARPi regimens (olaparib monotherapy, olaparib plus AAP, and talazoparib plus enzalutamide) were inappropriate due to the disconnected network, differences in trial populations related to effect modifiers, or imbalances within BRCA1/2 mutation-positive subgroups. The latter issue, coupled with the lack of a common comparator (except for olaparib plus AAP), also rendered anchored PAICs infeasible. Unanchored PAICs were either inappropriate due to lack of population overlap (vs. olaparib monotherapy) or were restricted by unmeasured confounders and small sample size (vs. olaparib plus AAP). PAIC versus talazoparib plus enzalutamide was not possible due to lack of published arm-level baseline characteristics and sufficient efficacy outcome data in the relevant population., Conclusion: The current randomized controlled trial evidence network does not permit robust comparisons between niraparib plus AAP and other PARPi regimens for patients with 1L BRCA-positive mCRPC. Decision-makers should scrutinize any ITC results in light of their limitations. Real-world evidence combined with clinical experience should inform treatment recommendations in this indication., (© 2024. The Author(s).)

Nearly all patients with multiple myeloma eventually relapse or become refractory to treatment. Lenalidomide is increasingly administered in the frontline until disease progression or intolerance to therapy, resulting in the need for highly effective, lenalidomide-sparing options. In this study, carfilzomib plus daratumumab and dexamethasone were evaluated against lenalidomide-sparing, pomalidomide-containing triplets using matching-adjusted indirect comparison in the absence of head-to-head data. The analyses utilized long-term follow-up data from the CANDOR study (NCT03158688). Treatment with carfilzomib, daratumumab, and dexamethasone resulted in significantly longer progression-free survival (hazard ratio 0.60 [95% confidence interval: 0.37, 0.88])vs. pomalidomide plus bortezomib and dexamethasone, and numerically longer progression-free survival (hazard ratio 0.77 [95% confidence interval: 0.50, 1.08]) vs. daratumumab plus pomalidomide and dexamethasone in patients with relapsed/refractory multiple myeloma and previous lenalidomide exposure, the majority of whom were lenalidomide refractory. Carfilzomib plus daratumumab and dexamethasone offers a highly effective, lenalidomide-sparing treatment option for this population.

Aim: National Institute for Health and Care Excellence guidance (Technical Support Document 19) highlights a key challenge of state transition models (STMs) being their difficulty in achieving a satisfactory fit to the observed within-trial endpoints. Fitting poorly to data over the trial period can then have implications for long-term extrapolations. A novel estimation approach is defined in which the predicted overall survival (OS) and progression-free survival (PFS) extrapolations from an STM are optimized to provide closer estimates of the within-trial endpoints. Materials & methods: An STM was fitted to the SQUIRE trial data in non-small-cell lung cancer (obtained from Project Data Sphere). Two methods were used: a standard approach whereby the maximum likelihood was utilized for the individual transitions and the best-fitting parametric model selected based on AIC/BIC, and a novel approach in which parameters were optimized by minimizing the area between the STM-predicted OS and PFS curves and the corresponding OS and PFS Kaplan-Meier curves. Sensitivity analyses were conducted to assess uncertainty. Results: The novel approach resulted in closer estimations to the OS and PFS Kaplan-Meier for all combinations of parametric distributions analyzed compared with the standard approach. Though the uncertainty associated with the novel approach was slightly larger, it provided better estimates to the restricted mean survival time in 10 of the 12 parametric distributions analyzed. Conclusion: A novel approach is defined which provides an alternative STM estimation method enabling improved fits to modeled endpoints, which can easily be extended to more complex model structures.