arXiv subjects

Lukas Pin

Publications and source records attributed to Lukas Pin.

8 recordsLinked to original sources

arXiv2025

Informed Burn-In Decisions in RAR: Harmonizing Adaptivity and Inferential Precision Based on Study Setting

Response-Adaptive Randomization (RAR) is recognized for its potential to deliver improvements in patient benefit. However, the utility of RAR is contingent on regularization methods to mitigate early instability and preserve statistical integrity. A standard regularization approach is the ''burn-in'' period, an initial phase of equal randomization before treatment allocation adapts based on accrued data. The length of this burn-in is a critical design parameter, yet its selection remains unsystematic and improvised, as no established guideline exists. A poorly chosen length poses significant risks: one that is too short leads to high estimation bias and type-I error rate inflation, while one that is too long impedes the intended patient and power benefits of using adaptation. The challenge of selecting the burn-in generalizes to a fundamental question: what is the statistically appropriate timing for the first adaptation? We introduce the first systematic framework for determining burn-in length. This framework synthesizes core factors - total sample size, problem difficulty, and two novel metrics (reactivity and expected final allocation error) - into a single, principled formula. Simulation studies, grounded in real-world designs, demonstrate that lengths derived from our formula successfully stabilize the trial. The formula identifies a ''sweet spot'' that mitigates type-I error rate inflation and mean-squared error, preserving the advantages of higher power and patient benefit. This framework moves researchers from conjecture toward a systematic, reliable approach.

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arXiv2025

A computational method for type I error rate control in power-maximizing response-adaptive randomization

Maximizing statistical power in experimental design often involves imbalanced treatment allocation, but several challenges hinder its practical adoption: (1) the misconception that equal allocation always maximizes power, (2) when only targeting maximum power, more than half the participants may be expected to obtain inferior treatment, and (3) response-adaptive randomization (RAR) targeting maximum statistical power may inflate type I error rates substantially. Recent work identified issue (3) and proposed a novel allocation procedure combined with the asymptotic score test. Instead, the current research focuses on finite-sample guarantees. First, we analyze the power for traditional power-maximizing RAR procedures under exact tests, including a novel generalization of Boschloo's test. Second, we evaluate constrained Markov decision process (CMDP) RAR procedures under exact tests. These procedures target maximum average power under constraints on pointwise and average type I error rates, with averages taken across the parametric space. A combination of the unconditional exact test and the CMDP procedure protecting allocations to the superior arm gives the best performance, providing substantial power gains over equal allocation while allocating more participants in expectation to the superior treatment. Future research could focus on the randomization test, in which CMDP procedures exhibited lower power compared to other examined RAR procedures.

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arXiv2025

Is 1:1 Always Most Powerful? Why Careful Determination of Allocation Ratios Matters in Trial Design

The principle of allocating an equal number of patients to each arm in a randomized controlled trial remains widely believed to be optimal for maximising statistical power. However, this long-held belief only holds true if the treatment groups have equal outcome variances, a condition that is often not met or, is simply not assessed in practice. This paper reasserts the fact that a departure from a 1:1 ratio can maintain or improve statistical power while increasing the benefits to participants. The benefit is particularly self-evident for binary and time-to-event endpoints, where variances are determined by the assumed success or event rates. To illustrate this, we present two case studies: a small-scale metastatic melanoma trial with a binary endpoint and a larger trial evaluating virtual reality for pain reduction with a continuous endpoint. Our simulations compare equal randomisation, preplanned fixed unequal randomisation, and response-adaptive randomisation targeting Neyman allocation. Results show that unequal allocation can increase the proportion of patients receiving the superior treatment without reducing power, with modest power gains observed in both binary and continuous settings, highlighting the practical relevance of optimised allocation strategies across trial types and sizes.

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arXiv2025

Safety-Driven Response Adaptive Randomisation: An Application in Non-inferiority Oncology Trials

The majority of response-adaptive randomisation (RAR) designs in the literature rely on efficacy data to guide dynamic patient allocation. However, their applicability becomes limited in settings where efficacy outcomes, such as survival, are observed with a random delay. To address this limitation, we introduce SAFER, a novel RAR design that leverages early-emerging safety data to inform treatment allocation decisions, particularly in oncology trials. The design is broadly applicable to contexts where prioritizing the arm with a superior safety is desirable. This is especially relevant in non-inferiority trials, to demonstrate that an experimental treatment is not inferior to the standard of care, while potentially offering improved tolerability. In such trials, an unavoidable trade-off arises: maintaining statistical efficiency for the efficacy hypothesis while integrating safety-driven adaptations through RAR. The SAFER design addresses this trade-off by dynamically adjusting the allocation proportion based on the observed association between safety and efficacy endpoints. We illustrate the performance of SAFER through a simulation study inspired by the CAPP-IT Phase III oncology trial. Results show that SAFER preserves statistical power, reduces the adverse event rate, and offers flexible adaptation speed depending on the temporal alignment of the endpoints.

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arXiv2025

A burn-in(g) question: How long should an initial equal randomization stage be before Bayesian response-adaptive randomization?

Response-adaptive randomization (RAR) can increase participant benefit in clinical trials, but also complicates statistical analysis. The burn-in period (a non-adaptive initial stage) is commonly used to mitigate this disadvantage, yet guidance on its optimal duration is scarce. To address this critical gap, this paper introduces an exact evaluation approach to investigate how the burn-in length impacts statistical operating characteristics of two-arm binary Bayesian RAR (BRAR) designs. We show that (1) commonly used calibration and asymptotic tests show substantial type I error rate inflation for BRAR designs without a burn-in period, and increasing the total burn-in length to more than half the trial size reduces but does not fully mitigate type I error rate inflation, necessitating exact tests; (2) exact tests conditioning on total successes show the highest average and minimum power up to large burn-in lengths; (3) the burn-in length substantially influences power and participant benefit, which are often not maximized at the maximum or minimum possible burn-in length; (4) the test statistic influences the type I error rate and power; (5) estimation bias decreases quicker in the burn-in length for larger treatment effects and increases for larger trial sizes under the same burn-in length. Our approach is illustrated by re-designing the ARREST trial.

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arXiv2025

Revisiting Optimal Allocations for Binary Responses: Insights from Considering Type-I Error Rate Control

This work revisits optimal response-adaptive designs from a type-I error rate perspective, highlighting when and how much these allocations exacerbate type-I error rate inflation - an issue previously undocumented. We explore a range of approaches from the literature that can be applied to reduce type-I error rate inflation. However, we found that all of these approaches fail to give a robust solution to the problem. To address this, we derive two optimal allocation proportions, incorporating the more robust score test (instead of the Wald test) with finite sample estimators (instead of the unknown true values) in the formulation of the optimization problem. One proportion optimizes statistical power and the other minimizes the total number failures in a trial while maintaining a fixed variance level. Through simulations based on an early-phase and a confirmatory trial we provide crucial practical insight into how these new optimal proportion designs can offer substantial patient outcomes advantages while controlling type-I error rate. While we focused on binary outcomes, the framework offers valuable insights that naturally extend to other outcome types, multi-armed trials and alternative measures of interest.

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arXiv2024

Thompson, Ulam, or Gauss? Multi-criteria recommendations for posterior probability computation methods in Bayesian response-adaptive trials

Bayesian adaptive designs enable flexible clinical trials by adapting features based on accumulating data. Among these, Bayesian Response-Adaptive Randomization (BRAR) skews patient allocation towards more promising treatments based on interim data. Implementing BRAR requires the relatively quick evaluation of posterior probabilities. However, the limitations of existing closed-form solutions mean trials often rely on computationally intensive approximations which can impact accuracy and the scope of scenarios explored. While faster Gaussian approximations exist, their reliability is not guaranteed. Critically, the approximation method used is often poorly reported, and the literature lacks practical guidance for selecting and comparing these methods, particularly regarding the trade-offs between computational speed, inferential accuracy, and their implications for patient benefit. In this paper, we focus on BRAR trials with binary endpoints, developing a novel algorithm that efficiently and exactly computes these posterior probabilities, enabling a robust assessment of existing approximation methods in use. Leveraging these exact computations, we establish a comprehensive benchmark for evaluating approximation methods based on their computational speed, patient benefit, and inferential accuracy. Our comprehensive analysis, conducted through a range of simulations in the two-armed case and a re-analysis of the three-armed Established Status Epilepticus Treatment Trial, reveals that the exact calculation algorithm is often the fastest, even for up to 12 treatment arms. Furthermore, we demonstrate that commonly used approximation methods can lead to significant power loss and Type I error rate inflation. We conclude by providing practical guidance to aid practitioners in selecting the most appropriate computation method for various clinical trial settings.

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arXiv2024

Randomization-based Inference for MCP-Mod

Dose selection is critical in pharmaceutical drug development, as it directly impacts therapeutic efficacy and patient safety of a drug. The Generalized Multiple Comparison Procedures and Modeling (MCP-Mod) approach is commonly used in Phase II trials for testing and estimation of dose-response relationships. However, its effectiveness in small sample sizes, particularly with binary endpoints, is hindered by issues like complete separation in logistic regression, leading to non-existence of estimates. Motivated by an actual clinical trial using the MCP-Mod approach, this paper introduces penalized maximum likelihood estimation (MLE) and randomization-based inference techniques to address these challenges. Randomization-based inference allows for exact finite sample inference, while population-based inference for MCP-Mod typically relies on asymptotic approximations. Simulation studies demonstrate that randomization-based tests can enhance statistical power in small to medium-sized samples while maintaining control over type-I error rates, even in the presence of time trends. Our results show that residual-based randomization tests using penalized MLEs not only improve computational efficiency but also outperform standard randomization-based methods, making them an adequate choice for dose-finding analyses within the MCP-Mod framework. Additionally, we apply these methods to pharmacometric settings, demonstrating their effectiveness in such scenarios. The results in this paper underscore the potential of randomization-based inference for the analysis of dose-finding trials, particularly in small sample contexts.

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