Likelihood Ratios Given Activity-Level Propositions for DNA Transfer Evidence: Practical Implementation and Simulation Studies Using the HaloGen Engine (Part II)

The interpretation of findings of low-template DNA given activity-level propositions requires robust statistical models capable of accommodating substantial inter-laboratory and case-specific variability. This paper presents the practical implementation of HaloGen, an open-source hierarchical Bayesian framework for calculating activity-level likelihood ratios (LRs) from DNA quantity data. We compare three modelling approaches derived from the framework: a Group model, which combines data across laboratories, a hierarchically informed Lab-Bayes model, and a standalone, laboratory specific Lab-Vague model. Through a series of simulation studies, we demonstrate that evidential strength is highly sensitive not only to DNA quantity but also to case context, particularly the assumed number of offenders (NS). We further show that inter-laboratory differences in DNA recovery and dropout can lead to materially different LRs, making unvalidated use of pooled or external data potentially misleading. To address practical implementation, we propose a minimum-effort validation pathway for laboratories wanting to report findings given activity level propositions. Our results indicate that a small number of direct/secondary transfer experiments (n {approx} 6- 12) are sufficient to obtain conservative LRs compared with a generic population model. Finally, these results clarify how contextual assumptions enter mathematically into activity-level inference, demonstrating that confirmation bias can arise naturally from unexamined modelling choices and underscoring the importance of transparent, explicit specification of propositions and parameters.