Artificial Intelligence Counterfactual Estimation of Antiretroviral Switching Thresholds Under Variable Viral Load Assay Turnaround: A Target Trial Emulation Evaluation of Healthcare Resistance Safety

Antiretroviral switching decisions in resource-limited settings depend on viral load results that arrive after variable delays, so the threshold that triggers a regimen change is applied to a measurement whose information is already stale. the present analysis formalises that decision as a target trial emulation in which the switching threshold is a counterfactual estimand and assay turnaround is a design parameter, and it characterises the operating characteristics of the before-after-control-impact estimator that such an emulation would use. No observational data are analysed; the study is a design and power analysis under a declared variance structure with between-cluster SD 6.0, residual SD 4.5 and intraclass correlation 0.64, evaluated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effects of 3, 5 and 8 percentage points.54 clusters are required to detect a 5 percentage point effect at 80 per cent power, that the minimum detectable effect falls steeply as clusters increase from 8 to 32, and that the estimator remains approximately unbiased with nominal interval coverage across the design grid. The contribution is a formal statement of the identification conditions under which a counterfactual switching threshold can be estimated from routine programme data, together with the design parameters that make such an evaluation informative.