Frank
Konietschke
Charité
Advancing Decision-Efficiency in (Pre)-clinical Research via Novel Sequential Frameworks
Abstract.
The escalating costs and ethical imperatives of drug development necessitate statistical frameworks that prioritize both efficiency and rigorous evidence generation. Conventional fixed-sample designs often lack the flexibility to adapt to emerging data, leading to potentially redundant animal testing or delayed decision-making. This talk introduces a suite of novel sequential testing procedures designed to optimize the transition from pre-clinical discovery to clinical validation. We explore three primary pillars of application: (1) Pre-clinical Statistics: We demonstrate how sequential probability ratio tests (SPRT) and group sequential designs can be adapted for small-sample laboratory studies, ensuring ethical stop-for-efficacy or stop-for-futility boundaries are met without compromising the Type I error rate. (2) Generalized Pairwise Comparisons (GPC): We extend the sequential framework to GPC, a versatile method for analyzing multiple prioritized endpoints. This allows researchers to assess the net clinical benefit of a treatment sequentially, integrating diverse outcomes (e.g., survival, toxicity, and biomarkers) into a single, unified decision metric. (3) Digital Biomarkers: Addressing the high-frequency, longitudinal nature of data from wearable devices, we propose sequential monitoring techniques that detect treatment signals in real-time. These methods account for the inherent noise and autocorrelation in digital health data, facilitating faster go/no-go decisions in early-phase trials. By bridging these methodologies, this talk provides a roadmap for implementing adaptive evidence synthesis that reduces sample size requirements and accelerates the identification of promising therapeutic candidates.