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This paper proposes a two-stage phase I-II clinical trial design to optimize doseschedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decisionmaking is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design’s performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.

Background Combination therapies involving novel agents, such as immunotherapies and targeted therapies, offer significant antitumor benefits by increasing dose intensity, targeting multiple pathways, and benefiting a broader patient population. To further explore these advantages, the National Cancer Institute (NCI) has initiated Combination Therapy Platform Trial with Molecular Analysis for Therapy Choice (ComboMATCH) to evaluate the effectiveness of new drug combinations in treating both adults and children. However, designing dose optimization trials for these combination therapies presents substantial challenges due to the complex interactions and unique mechanisms of action. Methods To address these challenges, we propose COMPACT, a Bayesian phase I-II randomized design for combination cancer therapies that uses progression-free survival (PFS) as the primary efficacy endpoint to identify the optimal dose combination (ODC) based on restricted mean survival time (RMST). The COMPACT design jointly evaluates both toxicity and PFS, with continuous toxicity monitoring throughout the trial. Toxicity probabilities are modeled using a partial ordering assumption without relying on complex parametric models, while PFS is modeled through a Bayesian Pareto proportional hazards model with gamma-shared frailty. The trial consists of two seamlessly connected stages. In the first stage, the dose space is explored primarily based on toxicity, while PFS data are concurrently collected. In the second stage, patients are adaptively randomized to safe and potentially promising dose combinations based on PFS, and the dose combination with the highest RMST among those deemed safe is selected as the ODC. Results Simulation studies demonstrate that COMPACT has desirable operating characteristics and outperforms conventional designs in identifying the ODC, allocating more patients to ODC, while maintaining patient safety. Sensitivity analysis is performed to examine the robustness of the proposed design. A trial example is provided to facilitate the practical implementation of the proposed COMPACT design. Conclusions The proposed COMPACT design offers a novel and robust framework for combination cancer therapies with progression-free survival end point.

Background Optimal Phase‐II design to evaluate new therapies in refractory/relapsed Ewing sarcomas (ES) remains imperfectly defined. Objectives Recurrent/refractory ES phase‐I/II trials analysis to improve trials design. Methods Comprehensive review of therapeutic trials registered on five databases (who.int/trialsearch, clinicaltrials.gov, clinicaltrialsregister.eu, e‐cancer.fr, and umin.ac.jp) and/or published in PubMed/ASCO/ESMO websites, between 2005 and 2018, using the criterion: (Ewing sarcoma OR bone sarcoma OR sarcoma) AND (Phase‐I or Phase‐II). Results The 146 trials identified (77 phase‐I/II, 67 phase‐II, and 2 phase‐II/III) tested targeted (34%), chemo‐ (23%), immune therapies (19%), or combined therapies (24%). Twenty‐three trials were ES specific and 48 had a specific ES stratum. Usually multicentric (88%), few trials were international (30%). Inclusion criteria cover the recurrent ES age range for only 12% of trials and allowed only accrual of measurable diseases (RECIST criteria). Single‐arm design was the most frequent (88%) testing mainly single drugs (61%), only 5% were randomized. Primary efficacy outcome was response rate (RR=CR+PR; Complete+Partial response) (n = 116/146; 79%), rarely progression‐free or overall survival (16% PFS and 3% OS). H0 and H1 hypotheses were variable (3%–25% and 20%–50%, respectively). The 62 published trials enrolled 827 ES patients. RR was poor (10%; 15 CR=1.7%, 68 PR=8.3%). Stable disease was the best response for 186 patients (25%). Median PFS/OS was of 1.9 (range 1.3–14.7) and 7.6 months (5–30), respectively. Eleven (18%) published trials were considered positive, with median RR/PFS/OS of 15% (7%–30%), 4.5 (1.3–10), and 16.6 months (6.9–30), respectively. Conclusion This review supports the need to develop the international randomized phase‐II trials across all age ranges with PFS as primary endpoint. Hundred and forty‐six phase‐II trials and 62 articles from 2005 to 2018 were analysed. Mostly multicentric, few trials were international (30%), only 5% were randomized. Results of 62 published trials enrolling 827 ES patients were disappointing. Combining chemotherapy with another agent is a promising strategy. International randomized trans‐age with PFS as primary endpoint could be promoted.

Paroxysmal nocturnal hemoglobinuria (PNH) is a rare, acquired hematologic disorder characterized by complement-mediated hemolysis. C5 inhibitors (eculizumab/ravulizumab) control intravascular hemolysis but do not prevent residual extravascular hemolysis. The newly approved complement inhibitor, pegcetacoplan, inhibits C3, upstream of C5, and has the potential to improve control of complement-mediated hemolysis. The PADDOCK and PALOMINO clinical trials assessed the safety and efficacy of pegcetacoplan in complement inhibitor-naïve adults (≥ 18 years) diagnosed with PNH. Patients in PADDOCK (phase 1b open-label, pilot trial) received daily subcutaneous pegcetacoplan (cohort 1: 180 mg up to day 28 [n = 3]; cohort 2: 270–360 mg up to day 365 [n = 20]). PALOMINO (phase 2a, open-label trial) used the same dosing protocol as PADDOCK cohort 2 (n = 4). Primary endpoints in both trials were mean change from baseline in hemoglobin, lactate dehydrogenase, haptoglobin, and the number and severity of treatment-emergent adverse events. Mean baseline hemoglobin levels were below the lower limit of normal in both trials (PADDOCK: 8.38 g/dL; PALOMINO: 7.73 g/dL; normal range: 11.90–18.00 g/dL), increased to within normal range by day 85, and were sustained through day 365 (PADDOCK: 12.14 g/dL; PALOMINO: 13.00 g/dL). In PADDOCK, 3 serious adverse events (SAE) led to study drug discontinuation, 1 of which was deemed likely related to pegcetacoplan and 1 SAE, not deemed related to study drug, led to death. No SAE led to discontinuation/death in PALOMINO. Pegcetacoplan was generally well tolerated and improved hematological parameters by controlling hemolysis, while also improving other clinical PNH indicators in both trials. These trials were registered at www.clinicaltrials.gov (NCT02588833 and NCT03593200).

While a general goal of early phase clinical studies is to identify an acceptable dose for further investigation, modern dose finding studies and designs are highly specific to individual clinical settings. In addition, as outcome‐adaptive dose finding methods often involve complex algorithms, it is crucial to have diagnostic tools to evaluate the plausibility of a method's simulated performance and the adequacy of the algorithm. In this article, we propose a simple technique that provides an upper limit, or a benchmark, of accuracy for dose finding methods for a given design objective. The proposed benchmark is nonparametric optimal in the sense of O'Quigley et al. (2002, Biostatistics 3, 51–56), and is demonstrated by examples to be a practical accuracy upper bound for model‐based dose finding methods. We illustrate the implementation of the technique in the context of phase I trials that consider multiple toxicities and phase I/II trials where dosing decisions are based on both toxicity and efficacy, and apply the benchmark to several clinical examples considered in the literature. By comparing the operating characteristics of a dose finding method to that of the benchmark, we can form quick initial assessments of whether the method is adequately calibrated and evaluate its sensitivity to the dose–outcome relationships.

Near-infrared spectroscopy-derived regional tissue oxygen saturation of haemoglobin (rSt O 2 ) reflects venous oxygen saturation. If cerebral metabolism is stable, rSt O 2 can be used as an estimate of cerebral oxygen delivery. The SafeBoosC phase II randomised clinical trial hypothesises that the burden of hypo- and hyperoxia can be reduced by the combined use of close monitoring of the cerebral rSt O 2 and a treatment guideline to correct deviations in rSt O 2 outside a predefined target range. Aims: To describe the rationale for and content of this treatment guideline. Methods: Review of the literature and assessment of the quality of evidence and the grade of recommendation for each of the interventions. Results and Conclusions: A clinical intervention algorithm based on the main determinants of cerebral perfusion-oxygenation changes during the first hours after birth was generated. The treatment guideline is presented to assist neonatologists in making decisions in relation to cerebral oximetry readings in preterm infants within the SafeBoosC phase II randomised clinical trial. The evidence grades were relatively low and the guideline cannot be recommended outside a research setting.

Background: The efficacy–toxicity trade-off based design is a practical Bayesian phase I–II dose-finding methodology. Because the design’s performance is very sensitive to prior hyperparameters and the shape of the target trade-off contour, specifying these two design elements properly is essential. Purpose: The goals are to provide a method that uses elicited mean outcome probabilities to derive a prior that is neither overly informative nor overly disperse, and practical guidelines for specifying the target trade-off contour. Methods: A general algorithm is presented that determines prior hyperparameters using least squares penalized by effective sample size. Guidelines for specifying the trade-off contour are provided. These methods are illustrated by a clinical trial in advanced prostate cancer. A new version of the efficacy–toxicity program is provided for implementation. Results: Together, the algorithm and guidelines provide substantive improvements in the design’s operating characteristics. Limitations: The method requires a substantial number of elicited values and design parameters, and computer simulations are required to obtain an acceptable design. Conclusion: The two key improvements greatly enhance the efficacy–toxicity design’s practical usefulness and are straightforward to implement using the updated computer program. The algorithm for determining prior hyperparameters to ensure a specified level of informativeness is general, and may be applied to models other than that underlying the efficacy–toxicity method.

Nivolumab is a fully human monoclonal antibody that inhibits programmed death‐1 activation. The clinical pharmacology profile of nivolumab was analyzed by a population pharmacokinetics model that assessed covariate effects on nivolumab concentrations in 1,895 patients who received 0.3–10.0 mg/kg nivolumab in 11 clinical trials. Nivolumab pharmacokinetics is linear with a time‐varying clearance. A full covariate model was developed to assess covariate effects on pharmacokinetic parameters. Nivolumab clearance and volume of distribution increase with body weight. The final model included the effects of baseline performance status (PS), baseline body weight, and baseline estimated glomerular filtration rate (eGFR), sex, and race on clearance, and effects of baseline body weight and sex on volume of distribution in the central compartment. Sex, PS, baseline eGFR, age, race, baseline lactate dehydrogenase, mild hepatic impairment, tumor type, tumor burden, and programmed death ligand‐1 expression had a significant but not clinically relevant (<20%) effect on nivolumab clearance.

A Bayesian two-stage phase I–II design is proposed for optimizing administration schedule and dose of an experimental agent based on the times to response and toxicity in the case where schedules are non-nested and qualitatively different. Sequentially adaptive decisions are based on the joint utility of the two event times. A utility function is constructed by partitioning the two-dimensional positive real quadrant of possible event time pairs into rectangles, eliciting a numerical utility for each rectangle, and fitting a smooth parametric function to the elicited values. We assume that each event time follows a gamma distribution with shape and scale parameters both modeled as functions of schedule and dose. A copula is assumed to obtain a bivariate distribution. To ensure an ethical trial, adaptive safety and efficacy acceptability conditions are imposed on the (schedule, dose) regimes. In stage 1 of the design, patients are randomized fairly among schedules and, within each schedule, a dose is chosen using a hybrid algorithm that either maximizes posterior mean utility or randomizes among acceptable doses. In stage 2, fair randomization among schedules is replaced by the hybrid algorithm. A modified version of this algorithm is used for nested schedules. Extensions of the model and utility function to accommodate death or discontinuation of follow up are described. The method is illustrated by an autologous stem cell transplantation trial in multiple myeloma, including a simulation study.