Interval estimation for minimal clinically important difference and its classification error via a bootstrap scheme
| Title: | Interval estimation for minimal clinically important difference and its classification error via a bootstrap scheme |
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| Authors: | Zehua Zhou, Jiwei Zhao, Melissa Kluczynski |
| Source: | Statistical Theory and Related Fields, Vol 4, Iss 2, Pp 135-145 (2020) |
| Publisher Information: | Taylor & Francis Group, 2020. |
| Publication Year: | 2020 |
| Collection: | LCC:Probabilities. Mathematical statistics |
| Subject Terms: | minimal clinically important difference, classification error, confidence interval, non-convex optimisation, bootstrap, m-out-of-n bootstrap, Probabilities. Mathematical statistics, QA273-280 |
| More Details: | With the improved knowledge on clinical relevance and more convenient access to the patient-reported outcome data, clinical researchers prefer to adopt minimal clinically important difference (MCID) rather than statistical significance as a testing standard to examine the effectiveness of certain intervention or treatment in clinical trials. A practical method to determining the MCID is based on the diagnostic measurement. By using this approach, the MCID can be formulated as the solution of a large margin classification problem. However, this method only produces the point estimation, hence lacks ways to evaluate its performance. In this paper, we introduce an m-out-of-n bootstrap approach which provides the interval estimations for MCID and its classification error, an associated accuracy measure for performance assessment. A variety of extensive simulation studies are implemented to show the advantages of our proposed method. Analysis of the chondral lesions and meniscus procedures (ChAMP) trial is our motivating example and is used to illustrate our method. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2475-4269 2475-4277 24754269 |
| Relation: | https://doaj.org/toc/2475-4269; https://doaj.org/toc/2475-4277 |
| DOI: | 10.1080/24754269.2019.1587692 |
| Access URL: | https://doaj.org/article/0456c8ed61fd4bc7b897dc91c35474c9 |
| Accession Number: | edsdoj.0456c8ed61fd4bc7b897dc91c35474c9 |
| Database: | Directory of Open Access Journals |
| ISSN: | 24754269 24754277 |
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| DOI: | 10.1080/24754269.2019.1587692 |
| Published in: | Statistical Theory and Related Fields |
| Language: | English |