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Open research questions in Statistical Methods in Clinical Trials

25 unresolved questions extracted from the limitations and future-work sections of 596 Statistical Methods in Clinical Trials papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • In our simulation study, we observed that the Clayton copula does not lead any invalid inference, whereas with the Frank and Gumbel copulas, the proposed methods might suffer from bias in estimation, poor control of the nominal level of the confidence interval and non-convergence in iteration steps for estimation probably due to difficulty to handle extra parameters in dependence with limited information from summary statistic data.

    Literature-based meta-analysis of adverse events accounting for heterogeneous follow-up duration in oncology clinical trials · 2026 · DOI

  • 76× relative to standard biomarker analysis, supports both clearance-modifying and injury-modifying mechanism inference, and accommodates sparse data.

    Efficacy inference in early-phase non-controlled clinical trials via Bayesian biomarker deconvolution · 2026 · DOI

  • However, when the normality, linearity, or the random-intercept assumption is violated, the validity and efficiency of the mixed-model ANCOVA estimators for estimating the average treatment effect remain unclear.

    On the Mixed-Model Analysis of Covariance in Cluster-Randomized Trials · 2026 · DOI

  • Although the mathematical results could, in principle, be extended to one-arm trials where 21 borrowing is performed on the treatment effect scale, exploring this application is beyond the scope of the current study. The demonstrated interplay between the prior weight 𝜔 and the robustification component 𝜋rob is not limited to the specific implementation proposed here but is also relevant to other approaches that rely on RMPs, including those based on empirical Bayes formulations such as the EB-rMAP22 and the SAM prior21.

    On the Interplay Between Prior Weight and Variance of the Robustification Component in Robust Mixture Prior Bayesian Dynamic Borrowing Approach · 2026 · DOI

  • The adaptive platform trial designs documented (HEALEY ALS Platform Trial, REMAP-CAP, PRINCIPLE trials) use hybrid or unspecified sample size methods, but there is insufficient evidence on optimal Bayesian sample size determination strategies specifically designed for multi-arm, multi-stage adaptive platform trial architectures with shared control arms.

    A systematic review of sample size determination in Bayesian randomized clinical trials: full Bayesian methods are rarely used · 2026 · DOI

  • The review documents extensive use of hybrid approaches across diverse outcome types (binary, continuous, ordinal, survival, joint, count, multiple endpoints), but does not systematically compare the performance or appropriateness of hybrid versus full Bayesian sample size methods across these specific outcome data structures. Research is needed to establish when hybrid methods are justified versus when full Bayesian approaches would be superior for each outcome type.

    A systematic review of sample size determination in Bayesian randomized clinical trials: full Bayesian methods are rarely used · 2026 · DOI

  • Many trials in the review are coded as 'No Justification' for their sample size methodology (e.g., Bolinski, Banooni, Bardia), indicating that a substantial proportion of clinical trials lack explicit documentation of their sample size determination approach. There is a need to investigate whether these trials employed Bayesian, frequentist, or hybrid methods but failed to report them, versus genuinely lacking formal sample size justification.

    A systematic review of sample size determination in Bayesian randomized clinical trials: full Bayesian methods are rarely used · 2026 · DOI

  • The systematic review identifies that full Bayesian methods for sample size determination are rarely used in randomized clinical trials, yet the included studies predominantly employ hybrid approaches combining Bayesian and frequentist elements. There is a specific gap in understanding why practitioners avoid fully Bayesian sample size determination methods and what methodological barriers or computational challenges prevent their adoption in clinical trial design.

    A systematic review of sample size determination in Bayesian randomized clinical trials: full Bayesian methods are rarely used · 2026 · DOI

  • Although the GEE can account for crossover specificities, it is limited by the availability of detailed trial information often encountered with reports of these trials.

    Meta‐analysis combining parallel and crossover trials using generalised estimating equation method · 2017 · DOI

  • For example, treatment dilution and treatment migration are common forms of randomization implementation failure in field experiments, and a review of the criminological literature on experiments reveals a lack of consensus as to how these problems should be handled when evaluating treatment effects.

    Dealing with Design Failures in Randomized Field Experiments: Analytic Issues Regarding the Evaluation of Treatment Effects · 1995 · DOI

  • 3% [26 of 45]) among the 45 published RCTs, yet no standardized way for presenting results was observed.

    Randomized Clinical Trials Using a Hierarchical Composite Primary End Point · 2026 · DOI

  • However, the impact of misclassifying patients into these groups is unclear, and strategies to improve HTE detection remain uncertain.

    Impact of subgroup classification accuracy on detecting heterogeneous treatment effects in Staphylococcus aureus bacteraemia: A simulation study · 2026 · DOI

  • Adaptive enrichment can allow the development of an experimental treatment to continue when its activity is insufficient in an all-comer population but remains promising in a prespecified biomarker-positive subgroup.

    A Globally Calibrated Bayesian Optimal Phase II Design for Adaptive Enrichment Trials · 2026

  • Class-switching evidence is inconclusive; linear-estimator results suggesting benefit did not survive doubly-robust estimation in small treated samples with limited propensity overlap.

    Escalate or Switch? Treating the Post-Titration GLP-1 Non-Responder: A Target Trial Emulation With Dose-Equivalence Reclassification · 2026 · DOI

  • Research Synthesis Methods 19 been employed in dose–response modelling24 and meta-analysis applications25 and could be explored for MBNMA.

    Model-based network meta-analysis: Joint estimation of dose–response and time–course relationships · 2026 · DOI

  • However, the access to the trial data, in particular sequence and period data in cross-over trials, remains a major limitation to the meta-analytic combination of trial designs.

    Meta‐analysis combining parallel and cross‐over trials with random effects · 2017 · DOI

  • Finally, the predictive ability of Bayesian models allows the estimation of the expected yield of performing additional studies, should the result of the current metaanalysis remain inconclusive.

    Bayesian statistics on the move · 2011 · DOI

  • However, the efficacy of group testing depends upon the use of a classification rule (that is, discard the samples in the pool, transfuse them or test them further) that is dependent on pool size, a characteristic that is lacking in currently implemented pooled testing procedures.

    Pooled Testing for HIV Screening: Capturing the Dilution Effect · 1996 · DOI

  • This being so, when little is known or it is desired to adopt an impartial stance about the object of inference before conducting a series of binary trials, applying a Bayesian approach to the predictive case is shown to suffice for the parametric case as well.

    On Prior Distributions for Binary Trials · 1984 · DOI

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25 open questions have been extracted from the limitations and future-work passages of 596 Statistical Methods in Clinical Trials papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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