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Ursula Garczarek

Publications and source records attributed to Ursula Garczarek.

2 recordsLinked to original sources

On the use of non-concurrent controls in platform trials: A scoping review

Platform trials gained popularity during the last few years as they increase flexibility compared to multi-arm trials by allowing new experimental arms entering when the trial already started. Using a shared control group in platform trials increases the trial efficiency compared to separate trials. Because of the later entry of some of the experimental treatment arms, the shared control group includes concurrent and non-concurrent control data. For a given experimental arm, non-concurrent controls refer to patients allocated to the control arm before the arm enters the trial, while concurrent controls refer to control patients that are randomised concurrently to the experimental arm. Using non-concurrent controls can result in bias in the estimate in case of time trends if the appropriate methodology is not used and the assumptions are not met. In this paper, we faced two main objectives. In the first, we aimed to identify the methods currently available for incorporating non-concurrent controls, clarify the key concepts and assumptions, and name the main characteristics of each method. For this purpose, we systematically searched research articles on methods to include non-concurrent controls. The second objective is to summarise the current regulatory view on non-concurrent controls to clarify the key concepts and current guidance. Therefore, we conducted a systematic search in regulatory guidelines regarding using non-concurrent controls and summarised the most relevant arguments and recommended methods. Finally, we discuss the advantages and potential caveats of using non-concurrent controls.

stat.ME↗

On the role of data, statistics and decisions in a pandemic

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy, and of effective dissemination and communication of findings.

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