Adjusted empirical likelihood analysis of restricted mean survival time for length-biased data

[1] Alemdjrodo, K., & Zhao, Y. (2020). New empirical likelihood inference for the mean residual life with length-biased and right-censored data. Journal of Nonparametric Statistics, 32(4), 1029-1046. https://doi.org/10.1080/10485252.2020.1840568

[2] Alemdjrodo, K., & Zhao, Y. (2022). Novel empirical likelihood inference for the mean di erence with right-censored data. Statistical Methods in Medical Research, 31(1), 87-104. https://doi.org/10.1177/09622802211041767

[3] Amiri, N., Fakoor, V., Sarmad, M., & Shariati, A. (2022). Empirical likelihood analysis for accelerated failure time model using length-biased data. Statistics, 56(3), 578-597. https://doi.org/10.1080/02331888.2022.2077334

[4] Andersen, P.K., Hansen, M.G., & Klein, J.P. (2004). Regression analysis of restricted mean survival time based on pseudo-observations. Lifetime data analysis, 10(4), 335-350. https://doi.org/10.1007/s10985-004-4771-0

[5] Asgharian, M., &Wolfson, D.B. (2005). Asymptotic behavior of the unconditional npmle of the length-biased survivor function from right censored prevalent cohort data. The Annals of Statistics, 33(5), 2109-2131. https://doi.org/10.1214/009053605000000372

[6] Bai, F., & Yang, R. (2023). Estimation of treatment e ects in two sample problems under general biased sampling data. Communications in Statistics-Theory and Methods, 1-13. https://doi.org/10.1080/03610926.2023.2273208

[7] Chen, J., & Huang, Y. (2013). Finite-sample properties of the adjusted empirical likelihood. Journal of Nonparametric Statistics, 25(1), 147-159. https://doi.org/10.1080/10485252.2012.738906

[8] Chen, J., Variyath, A.M., & Abraham, B. (2008). Adjusted empirical likelihood and its properties. Journal of Computational and Graphical Statistics, 17(2), 426-443. https://doi.org/10.1198/106186008X321068

[9] Cheng, Y.J, & Huang, C.Y. (2014). Combined estimating equation approaches for semiparametric transformation models with length-biased survival data. Biometrics, 70(3), 608-618. https://doi.org/10.1111/biom.12170

[10] Davison, A.C., & Hinkley, D.V. (1997). Bootstrap methods and their application. Number 1. Cambridge university press. https://doi.org/10.1080/00401706.2000.10486018

[11] Gach, F., & Potscher, B.M. (2011). Nonparametric maximum likelihood density estimation and simulation-based minimum distance estimators. Mathematical Methods of Statistics, 20, 288-326. https://doi.org/10.3103/S1066530711040028

[12] Hasegawa, T., Misawa, S., Nakagawa, S., Tanaka, S., Tanase, T., Ugai, H., Wakana, A., Yodo, Y., Tsuchiya, S., & Suganami. (2020). Restricted mean survival time as a summary measure of time-to-event outcome.  harmaceutical statistics, 19(4), 436-453. https://doi.org/10.1002/pst.2004

[13] He, S., & Huang, X. (2003). Central limit theorem of linear regression model under right censorship. Science in China Series A: Mathematics, 46(5), 600-610. https://doi.org/10.1360/02ys0139

[14] He, Y., & Zhou, Y. (2020). Nonparametric and semiparametric estimators of restricted mean survival time under length-biased sampling. Lifetime Data Analysis, 26, 761-788. https://doi.org/10.1007/s10985-020-09498-x

[15] Huang, C.Y., & Qin, J. (2011). Nonparametric estimation for length-biased and rightcensored data. Biometrika, 98(1), 177-186. https://doi.org/10.1093/biomet/asq069

[16] Hyde, J. (1977). Testing survival under right censoring and left truncation. Biometrika, 64(2), 225-230. https://doi.org/10.2307/2335688

[17] Keziou, A., & Leoni-Aubin, S. (2008). On empirical likelihood for semiparametric twosample density ratio models. Journal of Statistical Planning and Inference, 138(4), 915-928. https://doi.org/10.1016/j.jspi.2007.02.009

[18] Kiwitt, S., Nagel, E.R., & Neumeyer, N. (2008). Empirical likelihood estimators for the error distribution in nonparametric regression models. Mathematical Methods of Statistics, 17, 241-260. https://doi.org/10.3103/S1066530708030058

[19] Klein, J.P., & Moeschberger, M.L. (2003). Survival analysis: techniques for censored and truncated data, volume 1230. Springer. https://doi.org/10.1007/b97377

[20] Lee, C.H., Ning, J., & Shen, Y. (2018). Analysis of restricted mean survival time for length-biased data. Biometrics, 74(2), 575-583. https://doi.org/10.1111/biom.12772

[21] Liang, W., Shen, J.s., & He, S.y. (2016). Likelihood ratio inference for mean residual life of length-biased random variable. Acta Mathematicae Applicatae Sinica, English Series, 32(2), 269-282. https://doi.org/10.1007/s10255-016-0562-0

[22] Liu, Y., & Yu, C.W. (2010). Bartlett correctable two-sample adjusted empirical likelihood. Journal of Multivariate Analysis, 101(7), 1701-1711. https://doi.org/10.1016/j.jmva.2010.03.008

[23] Ning, J., Qin, J., Asgharian, M., & Shen, Y. (2013). Empirical likelihood-based condence intervals for length-biased data. Statistics in medicine, 32(13), 2278-2291. https://doi.org/10.1002/sim.5637

[24] Owen, A. (1990). Empirical likelihood ratio con dence regions. The annals of statistics, 18(1), 90-120. https://doi.org/10.1214/aos/1176347494

[25] Owen, A. (1991). Empirical likelihood for linear models. The Annals of Statistics, 1725-1747. https://doi.org/10.1214/aos/1176348368

[26] Owen, A.B. (2001). Empirical likelihood. Chapman and Hall/CRC. ISBN:9781420036152, 1420036157

[27] Qin, J., Ning, J., Liu, H., & Shen, Y. (2011). Maximum likelihood estimations and em algorithms with length-biased data. Journal of the American Statistical Association, 106(496), 1434-1449. https://doi.org/10.1198/jasa.2011.tm10156

[28] Shen, J., Yuen, K.C., & Liu, C. (2016). Empirical likelihood con dence regions for oneor two-samples with doubly censored data. Computational Statistics & Data Analysis, 93, 285-293. https://doi.org/10.1016/j.csda.2015.01.010

[29] Shi, J., Ma, H., & Zhou, Y. (2018). The nonparametric quantile estimation for length-biased and right-censored data. Statistics & Probability Letters, 134, 150-158. https://doi.org/10.1016/j.spl.2017.10.020

[30] Wang, Q. H., & Jing, B.Y. (2001). Empirical likelihood for a class of functionals of survival distribution with censored data. Annals of the Institute of Statistical Mathematics, 53, 517-527. https://doi.org/10.1023/A:1014617112870

[31] Wei, W., Wan, A.T., & Zhou, Y. (2018). Partially linear transformation model for length-biased and right-censored data. Journal of Nonparametric Statistics, 30(2), 332-367. https://doi.org/10.1080/10485252.2018.1424335

[32] Yang, H., Yau, C., &Zhao, Y. (2014). Smoothed empirical likelihood inference for the di erence of two quantiles with right censoring. Journal of Statistical Planning and Inference, 146, 95-101. https://doi.org/10.1016/j.jspi.2013.09.010

[33] Yang, X., Du, J., & Bai, F. (2023). Semiparametric inference of treatment e ects on restricted mean survival time in two sample problems from length-biased samples. Statistics & Probability Letters, 193, 109715. https://doi.org/10.1016/j.spl.2022.109715

[34] Yu, X. & Zhao, Y. (2019). Empirical likelihood inference for semi-parametric transformation models with length-biased sampling. Computational Statistics & Data Analysis, 132, 115-125. https://doi.org/10.1016/j.csda.2018.10.012

[35] Zhang, Y. (2018). A comparison of methods for estimating Restricted Mean Survival Time (Doctoral dissertation, Master's thesis, Leiden University.[100]).

[36] Zhang, C., Wu, Y., & Yin, G. (2020). Restricted mean survival time for interval-censored data. Statistics in Medicine, 39(26), 3879-3895. https://doi.org/10.1002/sim.8699

[37] Zhao, L., Claggett, B., Tian, L., Uno, H., Pfe er, M.A., Solomon, S.D., Trippa, L., & Wei, L. (2016). On the restricted mean survival time curve in survival analysis. Biometrics, 72(1), 215-221. https://doi.org/10.1111/biom.12384

[38] Zhao, Y., & Jinnah, A. (2012). Inference for cox's regression models via adjusted empirical likelihood. Computational Statistics, 27(1),1-12. https://doi.org/10.1007/s00180-010-0225-1

[39] Zheng, J., Shen, J., & He, S. (2014). Adjusted empirical likelihood for right censored lifetime data. Statistical Papers, 55(3), 827-839. https://doi.org/10.1007/s00362-013-0529-7

[40] Zhong, Y., Zhao, O., Zhang, B., & Yao, B. (2022). Adjusting for covariates in analysis based on restricted mean survival times. Pharmaceutical Statistics, 21(1), 38-54. https://doi.org/10.1002/pst.2151

[41] Zhou, M. (1992). Asymptotic normality of thesynthetic data'regression estimator for censored survival data. The Annals of Statistics, 1002-1021. https://doi.org/10.1214/aos/1176348667

[42] Zhou, M. (2021). Restricted mean survival time and con dence intervals by empirical likelihood ratio. Journal of Biopharmaceutical Statistics, 31(3), 362-374. https://doi.org/10.1080/10543406.2020.1862143

[43] Zhou, Y., & Liang, H. (2005). Empirical-likelihood-based semiparametric inference for the treatment e ect in the two-sample problem with censoring. Biometrika, 92(2), 271-282. https://doi.org/10.1093/biomet/92.2.271