OBJECTIVES: Treatment-to-Goal analyses are commonly used to predict population blood pressure (BP) control rates for antihypertensive agents based on mean BP lowering. However, control rates are frequently inaccurate because variability in BP reduction and baseline BPs are not considered. This study presents a new methodology that improves on population BP control estimates. METHODS: Untreated hypertensive patients (n=2483) from the Third National Health and Nutrition Examination Survey formed the test-sample. Monte Carlo simulation trials (MCST) of 500 patient-level BP reductions were generated from 3 underlying distributions: normal, lognormal, and beta. BP control, defined as SBP<140 and DBP<90 mmHg, was estimated by 3 methods: parametric- MCST-based means and variances were used to generate BP lowering data, assuming a normal distribution, and were subtracted from test-sample baseline BPs; point-estimate- mean BP reductions from MCST were directly subtracted from baseline BPs; bootstrapping- MCST BP reductions were bootstrapped with replacement and applied to the test-sample. Parametric and point-estimate results were compared to more comprehensive bootstrapping estimates for each simulation trial. We also investigated the relative performance of each method in the subgroup patients at three hypertension stages defined in the JNC VI guideline. RESULTS: We assumed a mean (+-SD) BP lowering of 20(12) and 14(7) mmHg systolic and diastolic. Parametric, bootstrapping, and point-estimate methods projected BP control rates of 66.9, 67.3, and 75.5%, respectively. The Point-estimate method frequently projected inaccurate control rates while the parametric results were shown consistent with the bootstrap method under a wide range of model conditions. CONCLUSIONS: Regardless of the underlying data distribution, parametric method provides more accurate control rates than point-estimate. Since patient-level BP reduction trial data are frequently unavailable to researchers, this parametric method can be used to generate more accurate treatment to goal analyses. This methodology can be extended to other therapeutic areas to estimate treatment effectiveness.