Speaker
Description
Virtual Control Groups (VCGs) represent an approach in which historical control data (HCD) from previous animal studies are used to replace animals in current control groups. The VICT3R project (Developing and implementing VIrtual Control groups To reducE animal use in toxicology Research), funded by the Innovative Health Initiative (IHI), aims to reduce the use of animals in toxicological research by implementing VCGs.
When using HCD to replace current control groups with VCGs, it is essential to carefully match the HCD with the characteristics of the current control groups. For achieving comparability between treated and control groups, reduction up to elimination of potential confounding effects is key. Therefore, a HCD pool of suitable control animals that could serve as VCGs must first be created by filtering and matching historical data to the legacy study. Established methods for selecting matching observations include clustering techniques and propensity score matching. VCGs can then be generated by sampling animals from this filtered HCD pool.
Typically, studies conducted within the last five years are considered when constructing VCGs in toxicological contexts. However, this fixed time window may not always reflect periods with stable observations. We therefore introduce a new approach to determine the time interval during which observations remain stable, using a method for changepoint detection. In the underlying model, a constant function is assumed between changepoints. The most recent estimated changepoint indicates the start of the time interval with stable values, which represent observations that could be used as virtual controls.
The proposed method is applied to data from the VICT3R database on male Sprague-Dawley rats from 28-day studies. To evaluate its performance, simulation studies are conducted by generating observations across multiple studies over time and introducing artificial changepoints into the data.
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