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Marta Regis

Publications and source records attributed to Marta Regis.

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EFSPI CMCSNE SIG position on the 'Expected f2'

The method called 'expected $f_2$' ($\hat{f}_{2,\exp}$), as proposed by Noce et al. (2020) and Xu et al. (2021), has been adopted in two health authority guidelines for dissolution profile comparison when variability precludes the use of the conventional similarity factor $\hat{f}_2$. This position paper, developed by a working group of the European Federation of Statisticians in the Pharmaceutical Industry CMC Statistical Network Europe Special Interest Group (EFSPI CMCSNE SIG), presents a critical evaluation of this method. Fundamental concerns are identified. First, the formula for $\hat{f}_{2,\exp}$ has no traceable origin in the references cited by its proponents. Noce et al. (2020) and Xu et al. (2021) attribute $\hat{f}_{2,\exp}$ to Shah et al. (1998) and Ma et al. (1999, 2000), but neither mentions nor suggests it. Second, no mathematical justification has been provided for the formula. Where Shah et al. (1998) subtract a variance term to reduce the upward bias of $\hat{f}_2$, the $\hat{f}_{2,\exp}$ formula adds this term, thereby increasing rather than correcting the bias. This has also been noted by FDA statisticians Liu et al. (2024). Third, the method exhibits poor statistical properties: for highly variable profiles, the variance term dominates the statistic, resulting in low power even as the true difference between profiles approaches zero. The method can reject equivalence when profiles are identical. Fourth, the formula as published by Noce et al. (2020) contains a notation ambiguity that renders the intended grouping of terms unclear. This ambiguity has propagated into regulatory guidance. A survey of working group members, designed to elicit arguments both for and against the method, found no scientifically meaningful advantage. The EFSPI CMCSNE SIG concludes that $\hat{f}_{2,\exp}$ should not be recommended for dissolution profile comparison.

stat.ME

Return-to-Baseline Testing via Empirically Calibrated e-processes

We consider the problem of detecting a Return to Baseline (RtB) in high-frequency monitoring data preceding and following an intervention, where the aim is to identify the time at which the data-generating distribution realigns with its pre-intervention distribution. We propose a sequential, distribution-free testing procedure that does not rely on specifying a null model and provides anytime-valid error control. The method relies on ideas from universal inference to define a discrepancy measure that is aggregated into a non-negative super-martingale, and is then empirically cal- ibrated to form an e-process. The calibration is performed using the baseline data, and is thus subject-specific. We establish finite-sample bounds for the calibration error (under a flexible non-parametric assumption), discuss the impact of tuning parameters and computational complexity, and illustrate through simulations and a clinical case study that the procedure accurately detects RtB from monitoring data.

stat.ME

Random autoregressive models: A structured overview

Models characterized by autoregressive structure and random coefficients are powerful tools for the analysis of high-frequency, high-dimensional and volatile time series. The available literature on such models is broad, but also sectorial, overlapping, and confusing. Most models focus on one property of the data, while much can be gained by combining the strength of various models and their sources of heterogeneity. We present a structured overview of the literature on autoregressive models with random coefficients. We describe hierarchy and analogies among models, and for each we systematically list properties, estimation methods, tests, software packages and typical applications.

stat.ME