11:00 John Stufken, George Mason University
Optimal Information-Based Subdata Selection
Subsampling or subdata selection is a crucial strategy when the size of large datasets exceeds available computing resources, or when observing the response variable is costly. The challenge is to select a subset of n data points from N available data points so that a maximum amount of information is retained. Since this is an NP hard problem, all solutions are only approximations of the optimal solution. For the various methods that have been proposed, little is known about the efficiency of the selected subdata relative to the optimal solution. Based on continuous optimal design theory, we propose a new method to bridge this gap. Through this approach we obtain a lower and upper bound for the relative efficiency of given subdata. We also develop a novel algorithm for subdata selection, show the convergence of the algorithm, and demonstrate its superior performance.
12:00 Andrea Ghiglietti, Università degli Studi Milano Bicocca
A system of urn models for incorporating informational borrowing in the design and inference of clinical trials
We present a new design methodology for stratified comparative experiments based on interacting urn systems. The key idea is to model the interaction between urns for borrowing information across strata and to use it in the design phase in order to i) enhance the information exchange at the beginning of the study, when only few subjects have been enrolled and the stratum-specific information on treatments’ efficacy could be scarce, ii) let the information sharing adaptively evolves via an updating mechanism based on the observed outcomes, for skewing at each step the allocations towards the most promising stratum-specific treatment and iii) make the contribution of the strata with different treatment efficacy vanishing as the stratum information grows. In particular, we introduce the interacting urns design, namely a new covariate-adjusted response-adaptive procedure, that randomizes the treatment allocations according to the evolution of the urn system. The theoretical properties of this proposal are described and the corresponding asymptotic inference is provided. Moreover, by a functional central limit theorem, we obtain the asymptotic joint distribution of the Wald type sequential test statistics, which allows to sequentially monitor the suggested design in clinical practice.
Collegamento Microsoft Teams
Finanziato dall'Unione Europea - NextGenerationEU a valere sul Piano Nazionale di Ripresa e Resilienza (PNRR) – Missione 4 Istruzione e ricerca – Componente 2 Dalla ricerca all’impresa - Investimento 1.1, Avviso Prin 2022 indetto con DD N. 104 del 2/2/2022, dal titolo [Optimal and adaptive designs for modern medical experimentation], codice proposta [2022TRB44L] - CUP [J53D23003270006]