Interval estimation for minimal clinically important difference and its classification error via a bootstrap scheme

Abstract

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.

Keywords:

• Minimal clinically important difference
• classification error
• confidence interval
• non-convex optimisation
• bootstrap
• m-out-of-n bootstrap

Acknowledgments

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UL1TR001412.

Notes on contributors

Zehua Zhou

Zehua Zhou is PhD candidate from the Department of Biostatistics at SUNY Buffalo. Before that, he obtained his Bachelor degree in Bioengineering from China Pharmaceutical University in 2014 and his Master degree in Biostatistics from SUNY Buffalo in 2017. He is interested in statistical machine learning methods, missing data analysis, and personalized medicine.

Jiwei Zhao

Jiwei Zhao is Assistant Professor from the Department of Biostatistics at SUNY Buffalo. He earned his PhD degree in Statistics from the University of Wisconsin-Madison in 2012. He is generally interested in using statistical and machine learning techniques to solve challenging problems in biomedical data science.

Melissa Kluczynski

Melissa Kluczynski is Clinical Research Associate from the Department of Orthopaedics at SUNY Buffalo. She is also affiliated with the UBMD Orthopaedics and Sports Medicine.