지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제31권 제6호
- 발행연도
- 2024.11
- 수록면
- 613 - 625 (13page)
이용수
초록· 키워드
오류제보하기
A general non-inferiority (NI) clinical trial is typically conducted using parametric testing methods with large samples. However, patient recruitment challenges often hinder rare disease trials, leading to enrollment failures. In this study, we introduce current parametric and nonparametric NI trial testing methods and propose modifications to enhance the performance of the nonparametric approach. Through a comprehensive simulation study with various sample sizes, data distributions, and sample ratios, we compare empirical levels and statistical powers as criteria for evaluating performance. Our findings indicate that the modified nonparametric methods outperformed the existing methods, particularly under conditions of small sample sizes and non-normal distributions, offering valuable insights for improving the reliability and sensitivity of NI trials in the context of rare diseases.
목차
Abstract
1. Introduction
2. Existing testing methods
3. Proposed methods
4. Simulation study
5. Conclusion
References
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Depth-Based rank test for multivariate two-sample scale problem
CSAM(Communications for Statistical Applications and Methods)
2023 .05
-
Sample size calculation for comparing time-averaged responses in K-group repeated binary outcomes
CSAM(Communications for Statistical Applications and Methods)
2018 .05
-
A Note on the Wilcoxon Score for the One-Sample Problem
Journal of The Korean Data Analysis Society
2015 .01
-
Nonparametric Bayesian methods: a gentle introduction and overview
CSAM(Communications for Statistical Applications and Methods)
2016 .11
-
Deep Multimodal Classification Model for Predicting Successes and Failures of Clinical Trials
Quantitative Bio-Science
2020 .05
-
Sequential patient recruitment monitoring in multi-center clinical trials
CSAM(Communications for Statistical Applications and Methods)
2018 .09
-
Local linear regression analysis for interval-valued data
CSAM(Communications for Statistical Applications and Methods)
2020 .05
-
A Nonparametric Test under the Proportional Hazards Model
Journal of The Korean Data Analysis Society
2016 .01
-
Sample Size Calculations for the Development of Biosimilar Products Based on Binary Endpoints
CSAM(Communications for Statistical Applications and Methods)
2015 .07
-
Estimation of long memory parameter in nonparametric regression
CSAM(Communications for Statistical Applications and Methods)
2019 .11
-
Nonparametric two sample tests for scale parameters of multivariate distributions
CSAM(Communications for Statistical Applications and Methods)
2020 .07
-
Computational issues on the blocked Gibbs algorithm
한국데이터정보과학회지
2023 .11
-
Nonparametric analysis of income distributions among different regions based on energy distance with applications to China Health and Nutrition Survey data
CSAM(Communications for Statistical Applications and Methods)
2019 .01
-
Nonparametric Bayesian copula modeling for shrinkage through the transformation
한국데이터정보과학회지
2023 .07
-
An approach based on clustering for detecting differentially expressed genes in microarray data analysis
CSAM(Communications for Statistical Applications and Methods)
2024 .09
-
Study on Nickel-Based Catalysts for Methane Pyrolysis Using Thermal Plasma
Applied Science and Convergence Technology
2024 .09
-
Nonparametric test for cointegration rank using Cholesky factor bootstrap
CSAM(Communications for Statistical Applications and Methods)
2016 .11
-
반복이 있는 랜덤화 블록 모형에서 정렬방법과 결합위치를 이용한 비모수 다중비교법
응용통계연구
2018 .10
-
Bayesian methods in clinical trials with applications to medical devices
CSAM(Communications for Statistical Applications and Methods)
2017 .11
-
Predicting the Outcomes of Clinical Trials with an Ensemble LS-SVR
Quantitative Bio-Science
2022 .05
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-151-25-02-091195424