Feasibility of Indirect Treatment Comparisons Between Niraparib Plus Abiraterone Acetate and Other First-Line Poly ADP-Ribose Polymerase Inhibitor Treatment Regimens for Patients with BRCA1/2 Mutation-Positive Metastatic Castration-Resistant Prostate Cancer.

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.

A methodological review of population-adjusted indirect comparisons reveals inconsistent reporting and suggests publication bias.

OBJECTIVES: Population-adjusted indirect comparisons (PAICs) were developed in the 2010s to allow for comparisons between two treatments evaluated in different trials while accounting for differences in patient characteristics if individual patient data (IPD) are available for only one trial. Such comparisons are increasingly used in market access applications when a pharmaceutical company compares its new treatment (with IPD available) to another treatment developed by a competitor (with only aggregated data available). This study aimed to describe the characteristics of these PAICs, assess their methodology, and describe the reported results. STUDY DESIGN AND SETTING: Original articles reporting the use of at least one PAIC were searched on PubMed between January 1, 2010 and April 2, 2022. Two reviewers independently selected articles and extracted data. RESULTS: We included 133 publications reporting the results of 288 PAICs. Half of the articles were published on or after May 7, 2020, and 71 (53%) pertained to onco-hematology. The pharmaceutical industry was involved in 130 (98%) articles. Key methodological aspects were reported inconsistently, with only three articles adequately reporting all aspects. A total of 161 (56%) articles reported a statistically significant benefit for the treatment evaluated on IPD. Conversely, only one PAIC significantly favored the treatment evaluated on aggregated data. CONCLUSION: Although the number of published PAICs is increasing, the methodology and transparency need to be improved. Moreover, our study strongly suggests a reporting bias. This situation calls for strengthening guidelines to improve trust in PAIC results and thus their reliability in market access applications.

A Comparison of Relative-Efficacy Estimate(S) Derived From Both Matching-Adjusted Indirect Comparisons and Standard Anchored Indirect Treatment Comparisons: A Review of Matching-Adjusted Indirect Comparisons.

OBJECTIVES: We present an empirical comparison of relative-efficacy estimate(s) from matching-adjusted indirect comparisons (MAICs) with estimates from corresponding standard anchored indirect treatment comparisons. METHODS: A total of 80 comparisons were identified from 17 publications through a systematic rapid review. A standardized metric that used reported relative treatment efficacy estimates and their associated uncertainty was used to compare the methods across different treatment indications and outcome measures. RESULTS: On aggregate, MAICs presented for connected networks tended to report a more favorable relative-efficacy estimate for the treatment for which individual-level patient data were available relative to the reported indirect treatment comparison estimate. CONCLUSIONS: Although we recognize the importance of MAIC and other population adjustment methods in certain situations, we recommend that results from these analyses are interpreted with caution. Researchers and analysts should carefully consider if MAICs are appropriate where presented and whether MAICs would have added value where omitted.

How Assessment-Schedule Matching Limits Bias When Comparing Progression-Free Survival in Single-Arm Studies: An Application in Second-Line Urothelial Carcinoma Treatments.

OBJECTIVES: Population-adjusted comparisons of progression-free survival (PFS) from single-arm trials of cancer treatments can be derived using matching-adjusted indirect comparisons (MAICs); however, results are still susceptible to bias, particularly if the trials had different tumor assessment schedules. This study aims to assess the effects of assessment-schedule matching (ASM) on the relative effectiveness on the PFS of avelumab versus approved comparator immunotherapies or chemotherapy after population matching in the second-line (2L) setting for metastatic urothelial carcinoma. METHODS: The MAIC used patient-level data for avelumab from the JAVELIN Solid Tumor trial (NCT01772004). PFS was compared with published curves for other treatments to obtain population-adjusted hazard ratios (HRs). The MAIC was repeated after conducting ASM for differences in tumor assessment scheduled first at 6 weeks for avelumab and durvalumab and at 8 or 9 weeks for other treatments. RESULTS: MAIC adjustment alone altered the HR estimates up to 23%, whereas MAIC plus ASM resulted in up to 32.7% reductions from naive comparisons. Even in cases in which MAIC had little effect, ASM brought an additional change of 11.1% to 15.4%. Overall, the HR range of avelumab versus other treatments changed from 0.83 to 1.25 for naive comparisons to 0.76 to 0.99 after ASM plus MAIC, numerically favoring avelumab. CONCLUSIONS: Small variations in assessment schedules can introduce bias in unanchored indirect treatment comparisons of interval-censored time-to-event outcomes. In this study, adjusted PFS was comparable across second-line urothelial carcinoma treatment options, numerically favoring avelumab versus immunotherapies and chemotherapy agents. Correcting this bias is especially important when HRs are applied in cost-effectiveness models to transition patients between states.

Difficulties arising in reimbursement recommendations on new medicines due to inadequate reporting of population adjustment indirect comparison methods.

Indirect treatment comparisons are useful to estimate relative treatment effects when head-to-head studies are not conducted. Statisticians at the National Centre for Pharmacoeconomics Ireland (NCPE) and Scottish Medicines Consortium (SMC) assess the clinical and cost-effectiveness of new medicines as part of multidisciplinary teams. We describe some shared observations on areas where reporting of population-adjustment indirect comparison methods is causing uncertainty in our recommendations to decision-making committees when assessing reimbursement of medicines.