PURPOSE: Mobocertinib is an oral epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that targets EGFR exon 20 insertion (ex20ins) mutations in non–small cell lung cancer (NSCLC). This open-label, phase III trial (EXCLAIM-2, ClinicalTrials.gov identifier: NCT04129502 ) compared mobocertinib versus platinum-based chemotherapy as first-line treatment of EGFR ex20ins+ advanced/metastatic NSCLC. METHODS: Patients with treatment-naive EGFR ex20ins+ locally advanced/metastatic NSCLC were randomly assigned 1:1 to mobocertinib 160 mg once daily or pemetrexed plus cisplatin or carboplatin every 3 weeks for four cycles followed by maintenance pemetrexed. The primary end point was progression-free survival (PFS) by blinded independent central review (BICR), with planned interim analysis (IA) after approximately 70% of 227 expected PFS events. RESULTS: A total of 354 patients were randomly assigned (mobocertinib: n = 179; chemotherapy: n = 175). Baseline characteristics were balanced between arms. At IA (cutoff: April 4, 2023), the median PFS per BICR was 9.6 months in each treatment arm (hazard ratio [HR], 1.04 [95% CI, 0.77 to 1.39]; P = .803). The primary end point crossed the prespecified futility boundary (HR > 1). The confirmed objective response rate (95% CI) per BICR was 32% (26 to 40) with mobocertinib versus 30% (24 to 38) with chemotherapy; the median duration of response was 12.0 versus 8.4 months. Quality-of-life assessments indicated clinically meaningful delays in time to deterioration of lung cancer symptoms, cognitive function, and constipation with mobocertinib versus chemotherapy. Grade ≥3 adverse events in >5% of patients (mobocertinib, chemotherapy) were diarrhea (20%, 1%), anemia (6%, 10%), increased lipase (6%, 0%), and decreased neutrophil count (1%, 7%). CONCLUSION: The EXCLAIM-2 trial did not meet its primary end point. The efficacy of mobocertinib was not superior to platinum-based chemotherapy for first-line treatment of patients with EGFR ex20ins+ advanced/metastatic NSCLC.

The amount of real-world data (RWD) available from sources other than randomized-controlled trials (RCTs) has grown ultra-rapidly in recent years. It provides the impetus for generating substantial evidence of effectiveness and safety from both RCTs and RWD to accelerate medical product development. Especially in the areas of unmet needs, the conduct of fully powered RCTs is generally infeasible because of their sizes, duration, cost, or ethical constraints. The unique challenges in such areas include a small patient population, heterogeneity in disease presentation, and a lack of established endpoints. However, merging information from disparate sources is an intricate task. The value of the Bayesian framework has gained more recognition due to its flexibility in calibrating uncertainty and handling data heterogeneity, and its inherent updating process ideal for synthesizing information. Meanwhile, propensity score, as a powerful tool in causal inference, can be used in various ways to adjust for confounders. As a newly emerging data borrowing strategy in a regulatory setting, integrating propensity scores in a Bayesian setting not only utilizes the strengths from Bayesian models but also minimizes bias from external data borrowing. These methods potentially allow information sharing among data sources, provide more reliable estimates when the sample size is small, and improve the efficiency of treatment effect estimation. In this paper, we will review the recent development of methods incorporating propensity score for evidence synthesis under the Bayesian framework, and discuss different examples of incorporating external data with or without RCTs, as well as the recommendations for reporting in clinical studies.

As the regulatory environment becomes progressively receptive toward utilizing real-world evidence, a spectrum of real-world data incorporation techniques in trial conduct and analysis has seen increasing interest and adoption in different stages of drug development. Of particular interest is leveraging external control data to augment the control arm in a concurrent randomized controlled trial, where patients are enrolled in both investigational treatment arm and the control arm. Yet despite the emerging literature in external data borrowing in a hybrid trial setting, very little discussion focuses on delineating what should be matched and what is actually being estimated, especially when a variety of matching schemes can be considered. In general, external control can be matched in four different ways: (1) matching with the intersection between investigational treatment and concurrent control, (2) matching with the union of concurrent investigational treatment and concurrent control, (3) matching with concurrent control alone, and (4) matching with investigational treatment alone. In this article, the formulation of estimands for different matching schemes are detailed to describe what these matching methods facilitate to answer. Simulation studies are also conducted to evaluate the performance characteristics under different matching schemes, estimation methods, effect size assumptions, and missingness of confounders.

For the clinical studies in rare diseases or small patient populations, having an adequately powered randomized controlled trial is further complicated by variability. As such, sample size re-estimation can be a useful tool if at an interim look the trial sample size needs to be increased to achieve adequate power to reject the null hypothesis. Meanwhile, borrowing or extrapolating information from real-world data or real-world evidence has gained increasing use in trial design and analysis since 2014. Combining these two strategies, high-quality real-world data, if leveraged properly, has the potential to generate real-world evidence that can assist interim decision-making, lower enrollment burden, and reduce study timeline and costs. With proper borrowing from historical control, some of the challenges in these high unmet medical need studies could be resolved considerably. We examine the incorporation of real-world evidence within the framework of adaptive design strategy in pediatric type II diabetes trials where recruitment has been challenging and the completion is hardly on time. Simulations under various scenarios are conducted to assess the borrowing strategy, i.e., the matching method in combination of sample size re-estimation. Comparisons of performance metrics are presented to showcase the advantages of proposed method.

In many therapeutic areas with unmet medical needs, such as pediatric oncology and rare diseases, one of the deterrent factors for clinical trial interpretability is the limited sample size with less-than-ideal operating characteristics. Single arm is usually the only viable design due to feasibility and ethical concerns. For the trial results to be more interpretable and conclusive, the evaluation of operating characteristics, such as type I error rate and power, and the appropriate utilization of prior information for study design, shall be prespecified and fully investigated during the trial planning phase. So far, very few existing literature addressed optimal sample size determination issues for the planning of pediatric and rare population trials, with majority of research focusing on analysis perspective with focus on Bayesian borrowing. In practice, when a single-arm trial is designed for rare population, it is not uncommon that the only information available is from an earlier trial and/or a few clinical publications based on observational studies, often constituting mixed or uncertain conclusions. In light of this, an optimal Bayesian sample size determination method for single-arm trial with binary or continuous endpoint is proposed, where conflicting prior beliefs can be readily incorporated. Prior effective sample size can be calculated to assess the robustness as well as the prior information borrowed. Moreover, due to the lack of closed-form posterior distributions in general, an alternative approach for calculating Bayesian power is described. Simulation studies are provided to demonstrate the utility of the proposed methods. In addition, a case study in pediatric patients with leukemia is included to illustrate the proposed method with the existing approaches.