Amikacin is one of the aminoglycosides with a narrow therapeutic window, significant dose-response relationship, and substantial interindividual pharmacokinetics (PK) variability, thus requiring an individualized dosing regimen. In this paper, a three-stage Bayesian hierarchical model was developed based on the known the PK parameters of amikacin obtained from a nonlinear mixed-effects model established for the Japanese population, and the weights were assigned to the priori and posteriori parts before two-dimensional Gibbs sampling, and simulations were performed using the data of 24 elderly patients with respiratory tract infections in Japan, after analyzing the predicted values and the range between effective trough concentrations (Ctrough) and peak concentrations (Cpeak), and residual plots, the dose for patients 3, 7, 9, and 16 was increased to 600 mg/day, and the dose for patient 20 was decreased to 400 mg/day, while keeping the remaining patients' doses unchanged, and the serum concentration at the time of the last administration was predicted, which showed that the Bayesian hierarchical model and the Markov chain Monte Carlo algorithm in this study performed well.

The selection of biomarker-specific patient populations is essential in targeted cancer therapies to enhance precision and efficacy. To ensure a successful launch, it is vital to promote awareness and adoption of biomarker testing at diagnosis, tailor implementation strategies to accommodate local variations, and ensure testing is accessible and reimbursed. An integrated evidence generation plan should address critical questions, including the prevalence of biomarker expression and agreement between local and central labs, across different platforms, antibodies and pathologists. Furthermore, understanding prognostic effects and associations with other biomarkers is of considerable interest. Real-world studies (RWS) play a pivotal role in addressing these questions but are inherently challenged by confounding factors, biases (e.g. immortal time bias), missing data, agreement assessment complexities, and low biomarker expression prevalence. This manuscript provides an integrated methodological roadmap for designing and analyzing RWS. We address key challenges by integrating robust statistical methodologies with advanced machine learning (ML) methods. Core methods discussed include the use of time-dependent Cox models to mitigate immortal time bias, inverse probability of biomarker weighting to adjust for confounding, and ML-based tree ensemble approaches to model complex relationships between covariates and outcomes and handle missing data. By systematically applying this integrated roadmap, we demonstrate how to enhance the validity and robustness of RW biomarker research. This approach overcomes common analytical pitfalls, enabling more reliable evidence generation for clinical decision-making. Ultimately, this roadmap helps drive precision oncology forward by ensuring that biomarker-driven therapeutic strategies are based on sound, high-quality RW evidence.

This paper introduces randomization-based analysis of covariance (RB-ANCOVA) for hypothesis testing of restricted mean survival time (RMST) differences between two randomized treatments in trials with categorized time-to-event data. RMST treatment differences over a prespecified time period are clinically meaningful and avoid assumptions like proportional hazards. The proposed method tests the strong null hypothesis that each participant's time-to-event would remain unchanged regardless of treatment assignment. Covariate adjustment is achieved by constraining baseline covariate means to be equal across arms, which reduces variance. The follow-up period is partitioned into mutually exclusive intervals, and RMST is approximated as the area under the survival curve. Under the strong null hypothesis, the joint asymptotic covariance structure of RMST and covariate means is known and used to construct a chi-squared test statistic for the covariate-adjusted RMST difference. The method accommodates stratified trial designs and supports hypothesis testing over single or several time intervals, enabling testing within internal intervals. The difference in RMSTs over an interval can be divided by its length to yield an average survival rate difference. Additionally, the approach allows the computation of essentially exact p-values via re-randomization. We illustrate the method with data from a randomized, placebo-controlled trial evaluating a test treatment for amyotrophic lateral sclerosis. This method provides a hypothesis testing approach for RMST treatment differences that leverages RB-ANCOVA to adjust for their correlations with baseline covariate differences. Under the strong null hypothesis, it enables hypothesis testing with clear control of Type I error and reduced variance without relying on model-based assumptions.

Phase II oncology trials often face challenges such as patient recruitment difficulties and limited resources, leading to the use of single-arm designs without concurrent controls. This study introduces a novel two-stage Bayesian design bridging single-arm and randomized two-arm trials for time-to-event endpoints. The design incorporates an initial experimental treatment stage with futility monitoring, followed by a randomized second stage comparing experimental and control arms, contingent on first-stage progression. The methodology enables comprehensive efficacy analysis by combining data from both stages. The design parameters are optimized to minimize the expected sample size under the null hypothesis of no treatment effect. Extensive simulation studies demonstrate consistently lower expected sample sizes than conventional two-arm trials utilizing log-rank tests. Furthermore, the design achieves reduced maximum sample sizes relative to the sequential implementation of separate single-arm and two-arm trials across various scenarios. An illustrative application in extensive-stage small cell lung cancer demonstrates practical implementation considerations. As a resource-efficient approach, this design streamlines phase II evaluations and expedites clinical decision-making for evaluating experimental oncology treatments.

The objective of phase 1 oncology dose‑escalation trials is to identify the dose with a toxicity rate that matches or is the closest to the target dose‑limiting toxicity (DLT). There are mainly two categories of design methods for phase 1 trials: the rule-based methods and model-based/assisted methods. All literature has focused on the designs, mostly on how the various model-based/assisted methods are better than the 3 + 3 design, which is the most used rule-based design. The accuracy of dose selection is perhaps the most important operating characteristics (OC). Our investigation shows that the accuracy is more consequentially affected by the maximum number of patients treated per dose (MPTPD) than by the type of designs. Simulation examples show that the model-based BOIN and CRM design has lower accuracy for dose selection than the 3 + 3 design with the same cohort size and the same MPTPD. Larger MPTPD is associated with larger sample sizes. Simulations show that selection accuracy improves with increasing MPTPD (hence sample sizes). Our findings suggest that the design of phase 1 trials should include consideration of the balance between MPTPD, sample size and expectation of selection accuracy. Rule-based designs that are tiered by cohort sizes and MPTPD are proposed. These designs perform similarly with the BOIN and CRM designs with identical cohort sizes and MPTPD, but can be used without any software.

Oncology dose optimization has moved beyond the maximum tolerated dose paradigm, yet many programs still implicitly target a threshold-style minimum effective dose (MED). In serious cancers, deliberately sub-therapeutic comparators are rarely ethical or approvable, so any sharp MED threshold is typically a fragile and only partially identifiable target. We propose a statistical and operational framework that reframes dose-finding as region-based optimization. First, we define a constraint-based Dose Optimization Region (DOR) where clinically meaningful benefit (potentially multi-endpoint and PD-informed) is credible, unacceptable toxicity is bounded, exposure targets are attainable within a prespecified window, and implementation is feasible. Second, within the DOR, we identify an Operational Optimal Dose (OOD) - a label-ready dosing strategy that integrates dose, schedule, exposure targets, and adjustmentrules - using comparative evidence across binary and time-to-eventendpoints, exposure - response (PK/PD) analyses, and time-to-event methods that account for delayed effects where follow-up allows. This approach turns regulatory evidence domains into region-definingconstraints and shifts the target from a single-point estimate to a strategy-level deliverable. It is intended to sit on top of conventional dose-finding designs and PK/PD modeling as a region-defining and reporting standard, rather than to replace existing methods. We illustrate its use with small-samplevisualizations based on pairwise superiority probabilities across in-region arms and with a real-world-inspired example to show how DOR→OOD can summarize the totality of evidence in practice. The framework provides statisticians and clinicians with practical design patterns and reporting checklists, aligning with contemporary regulatory expectations and supporting dose optimization in oncology, with potential adaptation to other therapeutic areas.

Propensity score matching (PSM) and augmented inverse propensity weighting (AIPW) are used in observational studies to estimate causal effects. The AIPW estimator is doubly robust and locally efficient but can be unstable when the propensity scores are close to zero or one. PSM circumvents the instability of propensity score weighting but it hinges on the correctness of the propensity score model and cannot attain the semiparametric efficiency bound. The fixed number of matches, K, renders PSM nonsmooth and thus invalidates standard bootstrap inference. This article presents novel augmented match weighted (AMW) estimators that combine the advantages of matching and weighting estimators. AMW adheres to the form of AIPW for its double robustness and local efficiency but it mitigates the instability. We replace inverse propensity weights with matching weights resulting from PSM with unfixed K. Meanwhile, we propose a new cross-validation procedure to select K that minimizes the mean squared error anchored around an unbiased estimator of the causal estimand. We derive the limiting distribution showing AMW estimators enjoy the double robustness property and can achieve the semiparametric efficiency bound if both nuisance models are correct. As a byproduct of unfixed K which smooths AMW estimators, nonparametric bootstrap can be adopted for variance estimation. Furthermore, simulation and real-data applications support that the AMW estimators are stable and their variances can be obtained by nonparametric bootstrap. Our method is particularly recommended when the propensity score distribution is extreme. In such settings, the AMW estimator is more efficient and robust than PSM, more stable than AIPW, and is accompanied by a valid variance estimation procedure.

HERALD (Holistic Evolving ReAssessment-Leveraged Decision-making) is a Bayesian decision-making framework anchored in the prediction of phase 3 efficacy success. At the proof-of-commercial-concept (POCC) study design stage, HERALD links available phase 3 design assumptions and success criteria with potential POCC treatment effects and yields decision boundaries that guarantee sufficient probability of success in phase 3. Since its commencement, HERALD has been widely adopted at Sanofi and received endorsement from the governance and key stakeholders across therapeutic areas. In this manuscript, we introduce how HERALD at the POCC design stage is industrialized. Through development of statistical software, standardization of governance presentation, consolidation of implementation examples, and periodical engagement programs of role-tailored trainings, we streamline the decision-making criteria discussion and empower both statisticians and nonstatisticians to communicate design options more efficiently within a cross-functional team.

Accurate sample size determination (SSD) is critical in clinical trial design, yet traditional methods that assume a fixed effect size overlooks estimation uncertainty, risking under- or over-powered studies. The assurance-based SSD approach, developed in earlier research, addresses this limitation by integrating statistical power over possible values of the effect size, weighted by their likelihood. Building on this foundation, we extend the framework to multi-regional clinical trials (MRCTs) using a hierarchical linear model (HLM) that can capture both between- and within-region variability in treatment effects. Analytical derivations and numerical studies show that assurance-based sample sizes are consistently larger than those from traditional approaches. Also, under high uncertainty, assurance-based sample sizes may not be defined, which serves as a valuable signal that the available evidence is insufficient to justify proceeding to confirmatory trials. These results demonstrate that the assurance approach provides a more reliable and transparent criterion for determining feasible sample sizes, highlighting its importance as a principled methodology, particularly in MRCTs where between-region variability further accentuates uncertainty in effect sizes.

By early 2023, there were over 100 gene, cell and RNA products approved globally (Chancellor et al. 2023). The way in which autologous cell and cell-based gene therapy products are administered can include multiple treatment stages, at times including leukapheresis, optional bridging therapy during manufacturing, conditioning regimen, and one or more doses of product. This brings important considerations in the development of the statistical analysis plan for clinical trials of autologous cell and cell-based gene therapies, including application of the estimand framework for key facets. In this article, we review all of the FDA currently approved autologous cell and cell-based gene therapy products as examples as they apply to general considerations for the statistical analysis plan as well as more in-depth discussion of treatment exposure, safety and efficacy analyses, and other aspects.

Conventional non-inferiority (NI) clinical trials often rely on large sample sizes and parametric methods with a single experimental drug. However, rare diseases pose challenges in obtaining large datasets, and data normality cannot always be assured. Nonparametric methods are a viable alternative, particularly when testing multiple experimental drugs with limited sample sizes. This paper reviews existing parametric and nonparametric NI testing methods and proposes a novel nonparametric NI method based on relative effect, a rank-based measure. Through simulations and real clinical trial data, we demonstrate that our proposed method outperforms existing ones in scenarios with small to moderate sample sizes and non-normal data. These findings highlight the potential of nonparametric approaches in NI clinical trials.

This manuscript provides an overview of recent and relevant dose-finding designs for cell and gene therapies (CGT). We start with an introduction of dose-finding. We then provide a brief overview of CGT, what dose‑escalation designs have already been developed to determine the maximum tolerated dose (MTD) and the optimal biological dose (OBD), and what dose‑escalation designs have been used so far in CGT approvals. We then discuss dose-finding design considerations and challenges for dose-finding for CGT.

Clinical trials often use composite endpoints to combine several clinically relevant outcomes into one treatment comparison. The win ratio is attractive for such endpoints because it compares patients hierarchically according to clinical priority, but the resulting overall ratio does not show how the individual components contribute to the reported treatment effect. Reporting all partial win ratios is informative but can be difficult to interpret because these partial summaries are not an additive decomposition of the overall win ratio. We propose SEWRA (Shapley-Enhanced Win Ratio Analysis), which applies the Shapley value to the log win ratio computed over subsets of components. On this scale, component contributions sum exactly to the overall log win ratio and can also be reported as multiplicative factors on the original win ratio scale. We position SEWRA as an attribution tool that complements, rather than replaces, the overall win ratio and component-specific clinical summaries. Simulations and a recurrent-event case study illustrate how SEWRA separates hierarchy-induced component contributions that are not apparent from partial win ratios alone.

It is increasingly common to augment randomized controlled trial with external controls from observational data, to evaluate the treatment effect of an intervention. Traditional approaches to treatment effect estimation involve ambiguous estimands and unrealistic or strong assumptions, such as mean exchangeability. We introduce a double-indexed notation for potential outcomes to define causal estimands transparently and clarify distinct sources of implicit bias. We show that the concurrent control arm is critical in assessing the plausibility of assumptions and providing unbiased causal estimation. We derive a consistent and locally efficient estimator for a class of weighted average treatment effect estimands that combines concurrent and external data without assuming mean exchangeability. This estimator incorporates an estimate of the systematic difference in outcomes between the concurrent and external units, of which we propose a Frisch-Waugh-Lovell style partial regression method to obtain. We compare the proposed methods with existing methods using extensive simulation and applied to cardiovascular clinical trials.