frequentist pathway mapping

Multiple testing in signal reproducibility detection faces challenges including severe multiple-testing burden and low statistical power in partial conjunction (PC) tests. To address these, we propose a covariate-driven adaptive grouping framework that leverages prior grouping information to enable cross-dataset information sharing, integrates covariate-guided feature screening with hypothesis weighting, and achieves stringent false discovery rate (FDR) control at ≤0.05 in finite samples while substantially improving statistical power. Our method introduces, for the first time, an adaptive filtering mechanism enabling independent weight learning for each hypothesis—overcoming the fundamental power limitations of conventional PC tests. Extensive simulations and analyses of real immune-related gene expression datasets demonstrate that our approach identifies 30–65% more significant reproducible signals than state-of-the-art multilevel testing methods, while rigorously maintaining the target FDR level.

Traditional gene-expression meta-analyses operate at the single-gene level, suffering from information loss and limited biological interpretability due to cross-platform gene absence and technical heterogeneity. To address this, we propose a pathway-level effect-size-driven meta-analysis paradigm. Our method introduces a novel aggregation strategy based on single-sample Gene Set Enrichment Analysis (ssGSEA), constructing pathway-level matrices that preserve both magnitude and directionality of effects. It integrates ssGSEA, effect-size-weighted meta-analysis, and R-based statistical modeling, implemented as an open-source CRAN R package. Validated across multiple datasets for systemic lupus erythematosus (SLE) and Parkinson’s disease, our approach significantly reduces false-positive rates while enhancing cross-platform comparability and biological interpretability of pathway activity—overcoming key limitations of conventional gene-centric meta-analysis.

This study addresses the underappreciated challenge of estimation and communication following multiplicity adjustment within the frequentist framework in complex clinical trials, where multiple endpoints, interim data looks, or group comparisons often introduce estimation bias and complicate interpretation, thereby undermining transparency in benefit–risk assessment. By integrating advanced methodologies such as adaptive designs and graphical approaches to multiple testing, the work illustrates through concrete examples the limitations of current strategies in conveying trial results meaningfully. The research underscores the need to critically reevaluate prevailing practices and foster interdisciplinary dialogue to enhance both the accuracy of effect estimation and the clarity of result communication, ultimately informing future methodological standards and regulatory guidance.

Classical false discovery rate (FDR) control methods, such as Benjamini–Hochberg (BH), rely on stringent pointwise control of Type I error (strong control), limiting their applicability under weaker inferential assumptions. This work addresses FDR control when only average-level (i.e., weak) control of significance level is required across tests. Method: We analyze the asymptotic FDR behavior of BH under average-type Type I error constraints and examine the finite-sample validity of the Benjamini–Yekutieli (BY) procedure for dependent p-values. Contribution/Results: We establish, for the first time, the asymptotic FDR control property of BH under weak Type I error control. We further prove that BY correction remains valid for dependent p-values even in finite samples. These results extend FDR theory to nonparametric, high-dimensional sparse, and weak-signal settings—bypassing traditional strong control assumptions—and substantially improve statistical power. The work provides a novel theoretical foundation and practical methodology for multiple testing under weak inference conditions.

In matched case-control studies, conventional statistical analyses of secondary outcomes can yield biased estimates by ignoring the unequal sampling probabilities induced by the matching design. This work proposes a novel likelihood-based approach that systematically incorporates the sampling structure inherent to matched designs, introducing sampling weights to produce unbiased estimation and valid inference for secondary outcomes. The method is theoretically guaranteed to deliver consistent estimators and confidence intervals with accurate coverage. Extensive simulations and an application to real-world diabetes data demonstrate its substantial superiority over existing methods. An R implementation of the proposed approach is publicly available.

This study addresses the challenge of constructing valid confidence intervals in two-stage adaptive enrichment clinical trials, where patient subgroups are selected based on interim data, thereby compromising the nominal coverage of conventional intervals. The authors propose a novel method that constructs confidence intervals conditional on the interim selection decision, leveraging conditional inference and inversion of uniformly most accurate unbiased (UMAU) tests to guarantee exact coverage within the selected subgroup. The approach is broadly applicable to various adaptive enrichment designs and is implemented via an efficient numerical algorithm. Extensive simulation studies demonstrate that the proposed intervals consistently achieve the desired coverage probability across diverse design configurations, substantially outperforming existing methods in both validity and precision.

This study addresses the selective bias in treatment effect estimation arising from data-driven subgroup selection in two-stage adaptive enrichment clinical trials, which causes conventional maximum likelihood estimators to overstate efficacy. The authors propose a general class of subgroup selection rules based on sample space partitioning and develop a unified analytical framework for constructing uniformly minimum variance conditionally unbiased estimators (UMVCUEs). This framework accommodates a broad range of selection mechanisms without requiring ad hoc derivations for each specific trial design. By integrating conditional unbiased estimation theory with sufficient statistics, the approach substantially enhances both generality and practical applicability. Extensive simulations demonstrate that the proposed UMVCUE effectively corrects selection-induced bias and accurately recovers the true treatment effect, thereby supporting more efficient and ethically sound drug development.

This study addresses a key challenge in multi-hypothesis group sequential clinical trials: how to provide informative simultaneous confidence intervals for effective treatment effect estimation while rigorously controlling the family-wise error rate (FWER). The authors propose a novel group sequential testing strategy that bases decisions solely on repeated p-values from the current stage and dynamically enhances significance thresholds by integrating evidence accumulated in prior stages. For the first time, they extend informative simultaneous confidence interval methodology to a graphical group sequential framework, combining the Bonferroni closure principle with repeated p-value methods. An iterative algorithm is developed to compute testing boundaries, complemented by precision assessment criteria and a conservative median estimation technique. The resulting approach maintains strict FWER control while substantially improving statistical power, with only minimal power loss attributable to the confidence intervals, and supports dynamic updating at each interim analysis stage.

This study addresses the issue of inflated global Type I error in adaptive enrichment clinical trials that separately calibrate Phase II designs for the overall population and the biomarker-positive subgroup. To resolve this, the authors propose a pathway-based globally calibrated Bayesian Optimal Phase II (BOP2) framework. At a pre-specified interim analysis, the design uses an futility boundary for the overall population to determine whether to switch to a subgroup-focused path, while jointly calibrating decision thresholds for both populations to control the overall false-positive rate. This approach achieves, for the first time, Bayesian joint calibration for branch-type adaptive enrichment designs, strictly controlling the global Type I error at any biomarker prevalence with all decision rules pre-specified. Leveraging posterior probability thresholds, a path-dependent calibration strategy, and an exact recursive enumeration algorithm for binary endpoints, the proposed design effectively maintains Type I error under the global null and outperforms conventional separate calibration methods, while appropriately supporting efficacy claims in either the full population or the subgroup across diverse treatment effect scenarios.