With the improved knowledge on clinical relevance and more convenient access to the patientreported outcome data,clinical researchers prefer to adopt minimal clinically important difference(MCID)rather than statistica...With the improved knowledge on clinical relevance and more convenient access to the patientreported 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.展开更多
In the Fay–Herriot model,we consider estimators of the linking variance obtained using different types of resampling schemes.The usefulness of this approach is that even when the estimator from the original data fall...In the Fay–Herriot model,we consider estimators of the linking variance obtained using different types of resampling schemes.The usefulness of this approach is that even when the estimator from the original data falls below zero or any other specified threshold,several of the resamples can potentially yield values above the threshold.We establish asymptotic consistency of the resampling-based estimator of the linking variance for a wide variety of resampling schemes and show the efficacy of using the proposed approach in numeric examples.展开更多
基金supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UL1TR001412.
文摘With the improved knowledge on clinical relevance and more convenient access to the patientreported 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.
基金This research is partially supported by the National Science Foundation(NSF)[grant numbers#DMS-1622483 and#DMS-1737918].
文摘In the Fay–Herriot model,we consider estimators of the linking variance obtained using different types of resampling schemes.The usefulness of this approach is that even when the estimator from the original data falls below zero or any other specified threshold,several of the resamples can potentially yield values above the threshold.We establish asymptotic consistency of the resampling-based estimator of the linking variance for a wide variety of resampling schemes and show the efficacy of using the proposed approach in numeric examples.
