Merging Adaptive Designs with Dynamic Infectious Disease Models Allows Faster and more Accurate Diagnostic Test Accuracy Studies in the Case of an Epidemic
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Background
During epidemics with emerging infections, diagnostic tests directly inform model-based decision-making and thereby shape infection control strategies. However, diagnostic accuracy studies (DTA) assessing the validity of these tests must be conducted under severe time and data constraints. We investigated whether the integration of adaptive designs and of epidemic spread modelling for prevalence prediction can accelerate DTA studies during epidemics with emerging infections without compromising statistical validity.
Methods
We compared three designs in a large-scale simulation study using a COVID-19 use case: a fixed design; a standard adaptive design with unblinded interim analysis enabling early stopping or sample size adaptation; and an adaptive design additionally integrating a prevalence projection model to inform sample size re-estimation. Data-generating mechanisms were based on infectious disease models and realistic recruitment constraints. As decision rules we used in one simulation line WHO criteria for DTA studies for COVID-19 and in the other one more liberal performance thresholds. Across 1,440 factorial scenarios (5,000 replications each), we evaluated study duration, sample size requirements, statistical power as well as bias in estimates.
Results
Both adaptive designs enabled substantial operational gains. For the WHO thresholds, early stopping (for futility or infeasibility) occurred in 80% of adaptive simulations; early efficacy stops were rare. Under more liberal thresholds, early termination was less frequent, leading to more studies reaching final analysis. Required sample sizes under WHO criteria frequently exceeded 10,000 participants, making fixed designs practically infeasible. Adaptive designs identified infeasible scenarios early and avoided continuation. Under liberal thresholds, recalculated sample sizes in adaptive designs closely tracked theoretical needs up to the upper quartile, in contrast to fixed designs mirroring the low power commonly observed in real-world pandemic studies. Overall, adaptive designs shortened study duration when stopping early and prevented continuation of unpromising trials.
Discussion
Adaptive designs in DTA studies during epidemics with emerging pathogens improve feasibility by preventing unrealistic recruitment targets and enabling early abandonment of non-viable scenarios. When realistic performance thresholds are used, adaptive re-estimation produces sample sizes more aligned with statistical requirements without systematic operational penalties. These findings support the adoption of adaptive approaches in confirmatory DTA studies for emerging infections as a pragmatic response to time pressure and uncertainty.
