arXiv subjects

Silvia Calderazzo

Publications and source records attributed to Silvia Calderazzo.

6 recordsLinked to original sources

arXiv2025

Information borrowing in Bayesian clinical trials: choice of tuning parameters for the robust mixture prior

External data borrowing in clinical trial designs has increased in recent years. This is accomplished in the Bayesian framework by specifying informative prior distributions. To mitigate the impact of potential inconsistency (bias) between external and current data, robust approaches have been proposed. One such approach is the robust mixture prior arising as a mixture of an informative prior and a more dispersed prior inducing dynamic borrowing. This prior requires the choice of four quantities: the mixture weight, mean, dispersion and parametric form of the robust component. To address the challenge associated with choosing these quantities, we perform a case-by-case study of their impact on specific operating characteristics in one-arm and hybrid-control trials with a normal endpoint. All four quantities were found to strongly impact the operating characteristics. As already known, variance of the robust component is linked to robustness. Less known, however, is that its location can have severe impact on test and estimation error. Further, the impact of the weight choice is strongly linked with the robust component's location and variance. We provide recommendations for the choice of the robust component parameters, prior weight, alternative functional form for this component and considerations for evaluating operating characteristics.

stat.ME↗

arXiv2025

Principled type I error rate inflation in two-arm clinical trial designs with external control information borrowing

External information borrowing is often considered in order to improve a clinical trial's efficiency. The Bayesian approach borrows such external information by specifying an informative prior distribution. A potential issue with this procedure is that external and current information may conflict, but such inconsistency may not be predictable a priori. Robust prior choices are typically proposed to limit extreme worsening of operating characteristics (OCs) in these situations. However, trade-offs are still present and in general strict control of type I error (TIE) rate prevents any power gains. In this context, principled justifications for TIE rate inflation can be of interest. We investigate two-arm trials, with a focus on external/historical control information borrowing. We illustrate OCs trade-offs and propose an interpretable approach for external information borrowing. The approach analytically links observed prior-data conflict with allowances for TIE rate inflation and power loss. The approach does not rely on a robust prior specification, but can instead be interpreted as an adaptive choice of Bayes - or, equivalently, frequentist - test decision thresholds under the available informative prior. In addition, it can be used to evaluate any dynamic borrowing approach from a frequentist testing standpoint, and to guarantee robustness with respect to misspecification of the data generating process (i.e., design prior) in Bayesian evaluations. A development for both Normal and binomial outcomes is provided.

stat.ME↗

arXiv2025

Large Sample Inference with Dynamic Information Borrowing

Large sample behavior of dynamic information borrowing (DIB) estimators is investigated. Asymptotic properties of several DIB approaches (adaptive risk minimization, adaptive LASSO, Bayesian procedures with empirical power prior, fully Bayesian procedures, and a Bayes-frequentist compromise) are explored against shrinking to zero alternatives. As shown theoretically and with simulations, local asymptotic distributions of DIB estimators are often non-normal. A simple Gaussian setting with external information borrowing illustrates that none of the considered DIB methods outperforms others in terms of mean squared error (MSE): at different conflict values, the MSEs of DIBs are changing between the MSEs of the maximum likelihood estimators based on the current and pooled data. To uniquely determine an optimality criterion for DIB, a prior distribution on the conflict needs be either implicitly or explicitly determined using data independent considerations. Data independent assumptions on the conflict are also needed for DIB-based hypothesis testing. New families of DIB estimators parameterized by a sensitivity-to-conflict parameter S are suggested and their use is illustrated in an infant mortality example. The choice of S is determined in a data-independent manner by a cost-benefit compromise associated with the use of external data.

stat.ME↗

arXiv2023

Simulating and reporting frequentist operating characteristics of clinical trials that borrow external information

Borrowing of information from historical or external data to inform inference in a current trial is an expanding field in the era of precision medicine, where trials are often performed in small patient cohorts for practical or ethical reasons. Many approaches for borrowing from external data have been proposed. Even though these methods are mainly based on Bayesian approaches by incorporating external information into the prior for the current analysis, frequentist operating characteristics of the analysis strategy are of interest. In particular, type I error and power at a prespecified point alternative are in the focus. It is well-known that borrowing from external information may lead to the alteration of type I error rate. We propose a procedure to investigate and report the frequentist operating characteristics in this context. The approach evaluates type I error rate of the test with borrowing from external data and calibrates the test without borrowing to this type I error rate. On this basis, a fair comparison of power between the test with and without borrowing is achieved.

stat.ME↗

arXiv2022

Robust incorporation of historical information with known type I error rate inflation

Bayesian clinical trials can benefit of available historical information through the elicitation of informative prior distributions. Concerns are however often raised about the potential for prior-data conflict and the impact of Bayes test decisions on frequentist operating characteristics, with particular attention being assigned to inflation of type I error rates. This motivates the development of principled borrowing mechanisms, that strike a balance between frequentist and Bayesian decisions. Ideally, the trust assigned to historical information defines the degree of robustness to prior-data conflict one is willing to sacrifice. However, such relationship is often not directly available when explicitly considering inflation of type I error rates. We build on available literature relating frequentist and Bayesian test decisions, and investigate a rationale for inflation of type I error rate which explicitly and linearly relates the amount of borrowing and the amount of type I error rate inflation in one-arm studies. A novel dynamic borrowing mechanism tailored to hypothesis testing is additionally proposed. We show that, while dynamic borrowing prevents the possibility to obtain a simple closed form type I error rate computation, an explicit upper bound can still be enforced. Connections with the robust mixture prior approach, particularly in relation to the choice of the mixture weight and robust component, are made. Simulations are performed to show the properties of the approach for normal and binomial outcomes.

stat.ME↗

arXiv2022

Coping with Information Loss and the Use of Auxiliary Sources of Data: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

Clinical trials disruption has always represented a non negligible part of the ending of interventional studies. While the SARS-CoV-2 (COVID-19) pandemic has led to an impressive and unprecedented initiation of clinical research, it has also led to considerable disruption of clinical trials in other disease areas, with around 80% of non-COVID-19 trials stopped or interrupted during the pandemic. In many cases the disrupted trials will not have the planned statistical power necessary to yield interpretable results. This paper describes methods to compensate for the information loss arising from trial disruptions by incorporating additional information available from auxiliary data sources. The methods described include the use of auxiliary data on baseline and early outcome data available from the trial itself and frequentist and Bayesian approaches for the incorporation of information from external data sources. The methods are illustrated by application to the analysis of artificial data based on the Primary care pediatrics Learning Activity Nutrition (PLAN) study, a clinical trial assessing a diet and exercise intervention for overweight children, that was affected by the COVID-19 pandemic. We show how all of the methods proposed lead to an increase in precision relative to use of complete case data only.

stat.AP↗