arXiv subjects

Stina Zetterstrom

Publications and source records attributed to Stina Zetterstrom.

3 recordsLinked to original sources

arXiv2026

Ready for the Clinic? A Survey of Open-Source Software for Response-Adaptive Randomization in Clinical Trials

Response-adaptive randomization (RAR) modifies treatment allocation probabilities during a clinical trial as response/outcome data accumulate, with the aim of improving patient benefit, statistical efficiency, or both. Despite substantial methodological development, adoption of RAR in clinical practice has remained limited, and the software available to support its design and implementation has not previously been reviewed. We identified 16 publicly available, open-source software packages implementing RAR methods, spanning urn-based, target-allocation, Bayesian, Markov decision process (MDP)-based, and dose-finding approaches, and evaluated them with respect to their methodological and practical characteristics. We found that while several software packages exist, most are method-specific, and only a small number provide broader, general-purpose adaptive-trial design and analysis capabilities. Explicit support for practically relevant features, including delayed and missing outcome data, temporal trends in response rates, flexible operating-characteristic evaluation, and platform or multi-arm multi-stage trial designs, is rare or absent across the identified software. These findings suggest that while a diverse set of tools exists for exploring RAR designs, gaps remain between the methodological literature and the software available to implement it in practice. Continued development of flexible, practically oriented, and validated software is important for the wider adoption of RAR in clinical research, and we highlight interesting areas of further work.

stat.CO↗

arXiv2026

SelectionBias: An R Package for Bounding Selection Bias in Causal Estimands

Selection bias may arise when there are dropouts or missing data in the analysis, or when subjects are included or excluded in the analysis based upon some selection criteria for the study population. Selection bias can jeopardize the validity of the study and a sensitivity analysis for assessing the effect of the selection is desired. Recently, there has been a surge of results for selection bias in causal inference, with several suggestions for sensitivity analyses. One method is to construct bounds for the bias. Here, we present the R package SelectionBias that can be used to calculate previously proposed bounds for selection bias for the causal risk ratio and causal risk difference for both the total and the selected populations. The first bound, derived by Smith and VanderWeele (SV), is based on values of sensitivity parameters that describe parts of the joint distribution of the outcome, treatment, selection indicator and unobserved variables. The second bound is an improved sharp bound that uses the same sensitivity parameters as the SV bound. The third bound is based solely on the observed data, and is therefore referred to as the assumption-free (AF) bound. The fourth and fifth bounds, the generalized assumption-free (GAF) and counterfactual assumption-free (CAF) bounds, utilize both the data and sensitivity parameters. The R package is illustrated with a simulated dataset that emulates a study where the effect of the zika virus on microcephaly in Brazil is investigated. Lastly, its performance and features are compared to the already existing R package EValue, highlighting situations where the two packages provide distinct advantages over each other.

stat.ME↗

arXiv2026

Operationalizing Allocation Probability Tests: Practical Guidance on Optimized Implementation for Power and Robustness

Recently, a new testing approach for response-adaptive clinical trials was proposed based on the allocation probabilities (AP) rather than the outcome data. While original work on the AP test focused on binary and normal endpoints and demonstrated that significant efficiency gains are possible, many critical questions remain open regarding its practical implementation and upper limits. In this work, rather than simply proposing novel statistics, we seek to understand the maximum gain that can be obtained with the AP test by optimizing how these probabilities are used to define the test statistic. We expand the method's practical utility by applying it to survival endpoints (exponential distributions) and introducing a rigorous strategy for selecting the null hypothesis to properly calibrate type I error. Our simulation studies reveal that by optimizing the functional form of the AP test, investigators can achieve a substantial increase in power, approaching the theoretical maximum, without sacrificing the patient outcome goals of the design. Furthermore, we explicitly compare the method to a standard Bayesian decision rule, finding that the optimized AP test significantly outperforms traditional frequentist tests while maintaining strict error control. This work provides a missing practical framework for implementing robust and optimized AP tests in complex response-adaptive settings.

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