14
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Regression analysis of asynchronous longitudinal data with informative dropout and dependent observation

ORCID Icon
Received 07 Apr 2023, Accepted 30 May 2024, Published online: 14 Jun 2024

References

  • Bromberger, J. T., L. L. Schott, H. M. Kravitz, M. Sowers, N. E. Avis, E. B. Gold, J. F. Randolph, and K. A. Matthews. 2010. Longitudinal change in reproductive hormones and depressive symptoms across the menopausal transition: Results from the Study of Women’s Health Across the Nation (SWAN). Archives of General Psychiatry 67 (6):598–607. doi:10.1001/archgenpsychiatry.2010.55.
  • Cao, H., D. Zeng, and J. P. Fine. 2015a. Regression analysis of sparse asynchronous longitudinal data. Journal of the Royal Statistical Society Series B: Statistical Methodology 77 (4):755–76. doi:10.1111/rssb.12086.
  • Cao, H., M. M. Churpek, D. Zeng, and J. P. Fine. 2015b. Analysis of the proportional hazards model with sparse longitudinal covariates. Journal of the American Statistical Association 110 (511):1187–96. doi:10.1080/01621459.2014.957289.
  • Chen, L., and H. Cao. 2017. Analysis of asynchronous longitudinal data with partially linear models. Electronic Journal of Statistics 11 (1):1549–69. doi:10.1214/17-EJS1266.
  • Chin, K. Y. 2018. The Relationship between follicle-stimulating hormone and bone health: Alternative explanation for bone loss beyond oestrogen? International Journal of Medical Sciences 15 (12):1373–83. doi:10.7150/ijms.26571.
  • Eleanor, M. P., and S. H. L. Lily. 2016. Longitudinal data subject to irregular observation: A review of methods with a focus on visit processes, assumptions, and study design. Statistical Methods in Medical Research 25:2992–3014.
  • Han, M., X. Song, L. Sun, and L. Liu. 2014. Joint modeling of longitudinal data with informative observation times and dropouts. Statistica Sinica 24:1487–504. doi:10.5705/ss.2013.063.
  • Kim, S. T., Faith, S., MaryFran, R. S., Robert, N., Lynda, P., Ellen, B. G., Gail, G., Gerson, W., Karen, M., Sonja, M., et al. 1996-2008. Study of women’s health across the nation (SWAN). https://www.swanstudy.org.
  • Li, Q., and L. Su. 2017. Accommodating informative dropout and death: A joint modelling approach for longitudinal and semicompeting risks data. Journal of the Royal Statistical Society Series C: Applied Statistics 67 (1):145–63. doi:10.1111/rssc.12210.
  • Lin, D. Y., and Z. Ying. 2001. Semiparametric and nonparametric regression analysis of longitudinal data. Journal of the American Statistical Association 96 (453):103–26. doi:10.1198/016214501750333018.
  • Lin, D. Y., and Z. Ying. 2003. Semiparametric regression analysis of longitudinal data with informative drop-outs. Biostatistics (Oxford, England) 4 (3):385–98. doi:10.1093/biostatistics/4.3.385.
  • Lin, D. Y., L. J. Wei, I. Yang, and Z. Ying. 2000. Semiparametric regression for the mean and rate functions of recurrent events. Journal of the Royal Statistical Society Series B: Statistical Methodology 62 (4):711–30. doi:10.1111/1467-9868.00259.
  • Molnar, F., M. Man-Son-Hing, B. Hutton, and D. Fergusson. 2009. Have last-observation-carried-forward analyses caused us to favour more toxic dementia therapies over less toxic alternatives? A systematic review. Open Medicine: A Peer-Reviewed, Independent, Open-Access Journal 3 (2):e31–e50.
  • Rogers, J. K., A. Yaroshinsky, S. J. Pocock, D. Stokar, and J. Pogoda. 2016. Analysis of recurrent events with an associated informative dropout time: Application of the joint frailty model. Statistics in Medicine 35 (13):2195–205. doi:10.1002/sim.6853.
  • Sun, D., H. Zhao, and J. Sun. 2021. Regression analysis of asynchronous longitudinal data with informative observation processes. Computational Statistics & Data Analysis 157:107161. doi:10.1016/j.csda.2020.107161.
  • Sun, J., L. Sun, and D. Liu. 2007. Regression analysis of longitudinal data in the presence of informative observation and censoring times. Journal of the American Statistical Association 102 (480):1397–406. doi:10.1198/016214507000000851.
  • Sun, L., X. Mu, Z. Sun, and X. Tong. 2011. Semiparametric analysis of longitudinal data with informative observation times. Acta Mathematicae Applicatae Sinica, English Series 27 (1):29–42. doi:10.1007/s10255-011-0037-2.
  • Tsiatis, A. A. 1990. Estimating regression parameters using linear rank tests for censored data. The Annals of Statistics 18 (1):354–72. doi:10.1214/aos/1176347504.
  • van der Vaart, A., and J. Wellner. 1996. Weak convergence and empirical processes, Springer, New York.
  • Wang, X., H. Zhang, Y. Chen, Y. Du, X. Jin, and Z. Zhang. 2020. Follicle stimulating hormone, its association with glucose and lipid metabolism during the menopausal transitiona. The Journal of Obstetrics and Gynaecology Research 46 (8):1419–24. doi:10.1111/jog.14297.
  • Wei, L. J., Z. Ying, and D. Y. Lin. 1990. Linear regression analysis of censored survival data based on rank tests. Biometrika 77 (4):845–51. doi:10.1093/biomet/77.4.845.
  • Yu, G., Y. Li, L. Zhu, H. Zhao, J. Sun, and L. L. Robison. 2019. An additive-multiplicative mean model for panel count data with dependent observation and dropout processes. Scandinavian Journal of Statistics 46 (2):414–31. doi:10.1111/sjos.12357.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.