Matches in UGent Biblio for { <https://biblio.ugent.be/publication/791138#aggregation> ?p ?o. }
Showing items 1 to 32 of
32
with 100 items per page.
- aggregation classification "A1".
- aggregation creator B506007.
- aggregation creator B506008.
- aggregation creator B506009.
- aggregation creator person.
- aggregation date "2009".
- aggregation hasFormat 791138.bibtex.
- aggregation hasFormat 791138.csv.
- aggregation hasFormat 791138.dc.
- aggregation hasFormat 791138.didl.
- aggregation hasFormat 791138.doc.
- aggregation hasFormat 791138.json.
- aggregation hasFormat 791138.mets.
- aggregation hasFormat 791138.mods.
- aggregation hasFormat 791138.rdf.
- aggregation hasFormat 791138.ris.
- aggregation hasFormat 791138.txt.
- aggregation hasFormat 791138.xls.
- aggregation hasFormat 791138.yaml.
- aggregation isPartOf urn:issn:1740-7745.
- aggregation language "eng".
- aggregation subject "Mathematics and Statistics".
- aggregation title "Linear and loglinear structural mean models to evaluate the benefits of an on-demand dosing regimen".
- aggregation abstract "Background Structural mean models can be used to estimate treatment efficacy when drug exposure varies. We applied stuctural 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. Methods 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. Results 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. Limitations Analysis could only be performed over the first 2 months after randomization because, afterwards, many patients dropped out from the placebo group. Conclusions 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.".
- aggregation authorList BK848873.
- aggregation endPage "415".
- aggregation issue "5".
- aggregation startPage "403".
- aggregation volume "6".
- aggregation isDescribedBy 791138.
- aggregation similarTo 1740774509344777.
- aggregation similarTo LU-791138.