Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 30, Number 2, April 2025
Page(s) 169 - 183
DOI https://doi.org/10.1051/wujns/2025302169
Published online 16 May 2025
  1. Alosh M, Huque M F, Bretz F, et al. Tutorial on statistical considerations on subgroup analysis in confirmatory clinical trials[J]. Statistics in Medicine, 2017, 36(8): 1334-1360. [Google Scholar]
  2. Sachdev J C, Sandoval A C, Jahanzeb M. Update on precision medicine in breast cancer[J]. Cancer Treatment and Research, 2019, 178: 45-80. [Google Scholar]
  3. Su X, Tsai C L, Wang H, et al. Subgroup analysis via recursive partitioning[J]. Journal of Machine Learning Research, 2009, 10(2): 141-158. [Google Scholar]
  4. Lipkovich I, Dmitrienko A, Denne J, et al. Subgroup identification based on differential effect search: A recursive partitioning method for establishing response to treatment in patient subpopulations[J]. Statistics in Medicine, 2011, 30(21): 2601-2621. [Google Scholar]
  5. Zhang Z H, Seibold H, Vettore M V, et al. Subgroup identification in clinical trials: An overview of available methods and their implementations with R[J]. Annals of Translational Medicine, 2018, 6(7): 122. [Google Scholar]
  6. Jiang Z Y, Du C G, Jablensky A, et al. Analysis of schizophrenia data using a nonlinear threshold index logistic model[J]. PLoS One, 2014, 9(10): e109454. [Google Scholar]
  7. Fan A L, Song R, Lu W B. Change-plane analysis for subgroup detection and sample size calculation[J]. Journal of the American Statistical Association, 2017, 112(518): 769-778. [Google Scholar]
  8. Vander Weele T J, Luedtke A R, Vander Laan M J, et al. Selecting optimal subgroups for treatment using many covariates[J]. Epidemiology, 2019, 30(3): 334-341. [Google Scholar]
  9. He Y, Lin H Z, Tu D S. A single-index threshold Cox proportional hazard model for identifying a treatment-sensitive subset based on multiple biomarkers[J]. Statistics in Medicine, 2018, 37(23): 3267-3279. [Google Scholar]
  10. Wei K C, Zhu H C, Qin G Y, et al. Multiply robust subgroup analysis based on a single-index threshold linear marginal model for longitudinal data with dropouts[J]. Statistics in Medicine, 2022, 41(15): 2822-2839. [Google Scholar]
  11. Cai Y Z, Stander J. Quantile self-exciting threshold autoregressive time series models[J]. Journal of Time Series Analysis, 2008, 29(1): 186-202. [Google Scholar]
  12. Galvao Jr A F, Montes‐Rojas G, Olmo J. Threshold quantile autoregressive models[J]. Journal of Time Series Analysis, 2011, 32(3): 253-267. [Google Scholar]
  13. Lee S, Seo M H, Shin Y. Testing for threshold effects in regression models[J]. Journal of the American Statistical Association, 2011, 106(493): 220-231. [Google Scholar]
  14. Su L J, Xu P. Common threshold in quantile regressions with an application to pricing for reputation[J]. Econometric Reviews, 2019, 38(4): 417-450. [Google Scholar]
  15. Zhang Y Y, Wang H J, Zhu Z Y. Single-index thresholding in quantile regression[J]. Journal of the American Statistical Association, 2022, 117(540): 2222-2237. [Google Scholar]
  16. Tibshirani R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 1996, 58(1): 267-288. [CrossRef] [Google Scholar]
  17. Tibshirani R, Saunders M, Rosset S, et al. Sparsity and smoothness via the fused lasso[J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2005, 67(1): 91-108. [Google Scholar]
  18. Zou H. The adaptive lasso and its oracle properties[J]. Journal of the American Statistical Association, 2006, 101(476): 1418-1429. [CrossRef] [Google Scholar]
  19. Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso[J]. Biostatistics, 2008, 9(3): 432-441. [CrossRef] [PubMed] [Google Scholar]
  20. Fan J Q, Li R Z. Variable selection via nonconcave penalized likelihood and its oracle properties[J]. Journal of the American Statistical Association, 2001, 96(456): 1348-1360. [CrossRef] [Google Scholar]
  21. Zhang Y, Lian H, Yu Y. Ultra-high dimensional single-index quantile regression[J]. Journal of Machine Learning Research, 2020, 21(224): 1-25. [Google Scholar]
  22. Ruppert D, Carroll R J. Theory & methods: Spatially-adaptive penalties for spline fitting[J]. Australian & New Zealand Journal of Statistics, 2000, 42(2): 205-223. [Google Scholar]
  23. Horowitz J L. A smoothed maximum score estimator for the binary response model[J]. Econometrica, 1992, 60(3): 505. [Google Scholar]
  24. Seo M H, Linton O. A smoothed least squares estimator for threshold regression models[J]. Journal of Econometrics, 2007, 141(2): 704-735. [Google Scholar]
  25. Zou H, Li R Z. One-step sparse estimates in nonconcave penalized likelihood models[J]. Annals of Statistics, 2008, 36(4): 1509-1533. [Google Scholar]
  26. Moore M J, Goldstein D, Hamm J, et al. Erlotinib plus gemcitabine compared with gemcitabine alone in patients with advanced pancreatic cancer: A phase III trial of the National Cancer Institute of Canada Clinical Trials Group[J]. Journal of Clinical Oncology, 2007, 25(15): 1960-1966. [Google Scholar]
  27. Shultz D B, Pai J, Chiu W, et al. A novel biomarker panel examining response to gemcitabine with or without erlotinib for pancreatic cancer therapy in NCIC clinical trials group PA.3[J]. PLoS One, 2016, 11(1): e0147995. [Google Scholar]
  28. Li J L, Jin B S. Multi-threshold accelerated failure time model[J]. The Annals of Statistics, 2018, 46(6A): 2657-2682. [Google Scholar]
  29. Li J L, Li Y G, Jin B S, et al. Multithreshold change plane model: Estimation theory and applications in subgroup identification[J]. Statistics in Medicine, 2021, 40(15): 3440-3459. [Google Scholar]
  30. Zhang L W, Wang H J, Zhu Z Y. Testing for change points due to a covariate threshold in quantile regression[J]. Statistica Sinica, 2014, 24(4): 1859-1877. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.