Linear and loglinear structural mean models to evaluate the benefits of an on-demand dosing regimen

Abstract

Structural mean models can be used to estimate treatment efficacy when drug exposure varies. We applied structural mean model to evaluate the clinical benefits of a proton pump inhibitor prescribed to be taken as needed to alleviate epigastric pain. We also investigated a new diagnostic approach to evaluate model assumptions. All patients were suffering from nonerosive reflux disease or functional ulcer-like dyspepsia and were prescribed a proton pump inhibitor to be taken as needed for relief of epigastric pain. The primary endpoint was a score variable that expresses the magnitude of gastro-intestinal symptoms at 8 weeks after randomization. We developed linear and loglinear versions of the structural mean models to derive an unbiased estimator of the reduction in symptom score as a function of exposure to the test drug. Semi-parametric models based on splines and corresponding simultaneous confidence bands identified the presence of potential interactions between drug exposure and baseline covariates. The on-demand dosing regimen generated a wide range of drug exposure. Application of SMM showed that the potential treatment-induced reduction in symptom score was much greater than the average treatment reduction observed in this population of patients. Our diagnostic tool was useful for detecting the interaction between drug exposure and baseline covariates. Analysis could only be performed over the first 2 months after randomization because, afterwards, many patients dropped out from the placebo group. The structural mean model approach allows one to estimate treatment efficacy in the presence of variable drug exposure. Similar results were obtained using linear and loglinear structural mean model.