×

Renewable risk assessment of heterogeneous streaming time-to-event cohorts. (English) Zbl 07925792

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
Full Text: DOI

References:

[1] KalbfleischJD, PrenticeRL. The Statistical Analysis of Failure Time Data. New York: John Wiley & Sons; 1980. · Zbl 0504.62096
[2] LiJ, MaS. Survival Analysis in Medicine and Genetics. New York: Chapman & Hall CRC Press; 2013. · Zbl 1378.62002
[3] WangC, ChenMH, SchifanoE, WuJ, YanJ. Statistical methods and computing for big data. Stat Interface. 2016;9(4):399‐414. · Zbl 1405.62004
[4] LuoL, SongPXK. Renewable estimation and incremental inference in generalized linear models with streaming data sets. J R Stat Soc Series B Stat Methodology. 2020;82(1):69‐97. · Zbl 1440.62288
[5] KleinJP, MoeschbergerML. Survival Analysis: Techniques for Censored and Truncated Data. New York: Springer‐Verlag; 2003. · Zbl 1011.62106
[6] RobbinsH, MonroS. A stochastic approximation method. Ann Math Stat. 1951;22(3):400‐407. · Zbl 0054.05901
[7] ToulisP, AiroldiEM. Scalable estimation strategies based on stochastic approximations: classical results and new insights. Stat Comput. 2015;25(4):781‐795. · Zbl 1332.62291
[8] ToulisP, AiroldiEM. Asymptotic and finite‐sample properties of estimators based on stochastic gradients. Ann Stat. 2017;45(4):1694‐1727. · Zbl 1378.62046
[9] FangY. Scalable statistical inference for averaged implicit stochastic gradient descent. Scand J Stat. 2019;46(4):987‐1002. · Zbl 1444.62135
[10] SchifanoED, WuJ, WangC, YanJ, ChenMH. Online updating of statistical inference in the big data setting. Dent Tech. 2016;58(3):393‐403.
[11] LuoL, ZhouL, SongPXK. Real‐time regression analysis of streaming clustered data with possible abnormal data batches. J Am Stat Assoc. 2023;118(543):2029‐2044. · Zbl 07751826
[12] WangK, WangH, LiS. Renewable quantile regression for streaming datasets. Knowl‐Based Syst. 2022;235:107675.
[13] JiangR, YuK. Renewable quantile regression for streaming data sets. Neurocomputing. 2022;508:208‐224.
[14] SunX, WangH, CaiC, YaoM, WangK. Online renewable smooth quantile regression. Comput Stat Data Anal. 2023;185:107781. · Zbl 1543.62197
[15] WangT, ZhangH, SunL. Renewable learning for multiplicative regression with streaming datasets. Comput Stat. 2024;39:1559‐1586. doi:10.1007/s00180‐023‐01360‐6
[16] MaX, LinL, GaiY. A general framework of online updating variable selection for generalized linear models with streaming datasets. J Stat Comput Simul. 2023;93(3):325‐340. · Zbl 07677420
[17] HectorEC, LuoL, SongPXK. Parallel‐and‐stream accelerator for computationally fast supervised learning. Comput Stat Data Anal. 2023;177:107587. · Zbl 1543.62085
[18] HanR, LuoL, LinY, HuangJ. Online inference with debiased stochastic gradient descent. Biometrika. 2024;111(1):93‐108.
[19] LinL, LiW, LuJ. Unified rules of renewable weighted sums for various online updating estimations. arXiv preprint arXiv:2008.08824. 2020.
[20] CoxDR. Regression models and life tables (with discussion). J R Stat Soc Series B Stat Methodology. 1972;34(2):187‐202.
[21] WuJ, ChenMH, SchifanoED, YanJ. Online updating of survival analysis. J Comput Graph Stat. 2021;30(4):1209‐1223. · Zbl 07499947
[22] GrambschPM, TherneauTM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81(3):515‐526. · Zbl 0810.62096
[23] XueY, WangH, YanJ, SchifanoED. An online updating approach for testing the proportional hazards assumption with streams of survival data. Biometrics. 2020;76(1):171‐182. · Zbl 1451.62149
[24] LuoL, SongPXK. Multivariate online regression analysis with heterogeneous streaming data. Can J Stat. 2023;51(1):111‐133. · Zbl 07759522
[25] ZouH. The adaptive lasso and its oracle properties. J Am Stat Assoc. 2006;101(476):1418‐1429. · Zbl 1171.62326
[26] ZhangHH, LuW. Adaptive lasso for Cox’s proportional hazards model. Biometrika. 2007;94(3):691‐703. · Zbl 1135.62083
[27] ZhangH, DengL, SchiffmanM, QinJ, YuK. Generalized integration model for improved statistical inference by leveraging external summary data. Biometrika. 2020;107(3):689‐703. · Zbl 1451.62152
[28] HanB, Van KeilegomI, WangX. Semiparametric estimation of the nonmixture cure model with auxiliary survival information. Biometrics. 2022;78(2):448‐459. · Zbl 1520.62218
[29] QinJ, LiuY, LiP. A selective review of statistical methods using calibration information from similar studies. Stat Theory Relat Fields. 2022;6(3):175‐190. · Zbl 1529.62031
[30] DingJ, LiJ, HanY, McKeagueIW, WangX. Fitting additive risk models using auxiliary information. Stat Med. 2023;42(6):894‐916.
[31] DingJ, LiJ, WangX. Efficient auxiliary information synthesis for cure rate model. J R Stat Soc Ser C Appl Stat. 2024;42(6):894‐916.
[32] AndersenPK, GillRD. Cox’s regression model for counting processes: a large sample study. Ann Stat. 1982;10(4):1100‐1120. · Zbl 0526.62026
[33] WeiJ, YangJ, ChengX, DingJ, LiS. Adaptive regression analysis of heterogeneous data streams via models with dynamic effects. Mathematics. 2023;11(24):4899.
[34] FanJ, LiR. Variable selection via nonconcave penalized likelihood and its Oracle properties. J Am Stat Assoc. 2001;96(456):1348‐1360. · Zbl 1073.62547
[35] ZhangCH. Nearly unbiased variable selection under minimax concave penalty. Ann Stat. 2010;38(2):894‐942. · Zbl 1183.62120
[36] McCullaghP, NelderJA. Generalized Linear Models. New York: Routledge; 2019.
[37] WangX, SongL. Adaptive lasso variable selection for the accelerated failure models. Commun Stat Theory Methods. 2011;40(24):4372‐4386. · Zbl 1239.62129
[38] ZhengJ, ZhengY, HsuL. Risk projection for time‐to‐event outcome leveraging summary statistics with source individual‐level data. J Am Stat Assoc. 2022;117(540):2043‐2055. · Zbl 1515.62125
[39] KeidingN, LouisTA. Perils and potentials of self‐selected entry to epidemiological studies and surveys. J R Stat Soc Ser A: Stat Soc. 2016;179(2):319‐376.
[40] SchottJR. Matrix Analysis for Statistics. New York: John Wiley & Sons; 2016.
[41] TibshiraniR. Regression shrinkage and selection via the lasso. J R Stat Soc Series B Stat Methodology. 1996;58(1):267‐288. · Zbl 0850.62538
[42] FriedmanJ, HastieT, TibshiraniR. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1‐22.
[43] FurrukhM. Tobacco smoking and lung cancer: perception‐changing facts. Sultan Qaboos Univ Med J. 2013;13(3):345‐358.
[44] BrayF, FerlayJ, SoerjomataramI, SiegelRL, TorreLA, JemalA. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394‐424.
[45] HuangX, LuoZ, LiangW, et al. Survival nomogram for young breast cancer patients based on the SEER database and an external validation cohort. Ann Surg Oncol. 2022;29:5772‐5781.
[46] LiJ, JinB. Multi‐threshold accelerated failure time model. Ann Stat. 2018;46(6A):2657‐2682. · Zbl 1410.62125
[47] WangB, LiJ, WangX. Multi‐threshold proportional hazards model and subgroup identification. Stat Med. 2022;41(29):5715‐5737.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.