Development of a Parametric Simulation Model for Forecasting Goal-Oriented Treatment Outcomes
| Autor: | Gilbert L'Italien, Yong Yuan, Roland Chen, Robert Karaniewsky |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Adult
Clinical Trials as Topic Adolescent National Health and Nutrition Examination Survey Health Policy Public Health Environmental and Occupational Health Blood Pressure Confidence interval Standard deviation Assumed mean Treatment Outcome Blood pressure Hypertension Log-normal distribution Statistics Humans Computer Simulation Forecasting Probability Test data Mathematics Parametric statistics |
| Zdroj: | Value in Health. 7(4):482-489 |
| ISSN: | 1098-3015 |
| DOI: | 10.1111/j.1524-4733.2004.74011.x |
| Popis: | Background: Treatment-to-goal (TTG) analyses are frequently used to predict guideline-directed population control rates for drug therapies based on mean efficacy data. Nevertheless, estimates are commonly inaccurate because variability in efficacy is not considered. A new methodology was developed to improve TTG forecasting. Methods: Patient-level blood pressure (BP) lowering data sets, designed to simulate clinical trial results, were generated for testing from three underlying distributions: normal, lognormal, and beta. To emulate real-world conditions where patient-level data are unavailable, two approaches were considered: parametric—simulated BP lowering data were generated using the mean and standard deviation of the test data sets; and point-estimate—BP lowering was uniformly assigned as the mean lowering. BP control (systolic BP < 140 and diastolic BP < 90 mmHg) was forecasted by subtracting values generated by these two methods from baseline BP values in untreated hypertensive patients (n = 2483) from the Third National Health and Nutrition Examination Survey. Estimated control rates were compared to analyses where the patient-level data sets were bootstrapped. Results: We assumed mean (± SD) BP lowering of 20 (12) mmHg systolic and 14 (7) mmHg diastolic. Parametric method predicted a BP control rate of 66.9% [95% confidence interval (CI) 65.7–67.9], similar to the bootstrapping approach (67.3%, 95% CI 65.9–68.8). The control rate projected based on the point-estimate method was 75.5%. The point-estimate method frequently led to substantially different results under a wide range of model assumptions. Conclusions: A new parametric-based forecasting method, which addresses underlying variability, improves on estimates based on mean efficacy only. In the absence of patient-level data, this method is generalizable to dif |
| Databáze: | OpenAIRE |
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