533
Views
1
CrossRef citations to date
0
Altmetric
Articles

D-optimal population designs in linear mixed effects models for multiple longitudinal data

&
Pages 88-94 | Received 25 Jun 2020, Accepted 29 Jan 2021, Published online: 12 Feb 2021

References

  • Atkinson, A. C., Donev, A. N., & Tobias, R. D. (2007). Optimum experimental designs, with SAS. Oxford University Press.
  • Azurduy, S. A. O. (2009). Robust designs for longitudinal studies. University of Maastricht.
  • Berger, M. P. F., Ouwens, M. J. N. M., & Tan, F. E. S. (2003). Robust designs for longitudinal mixed effects models. In H. Yanai, A. Okada, K. Shigemasu, Y. Kano, & J. J. Meulman (Eds.), New developments in psychometrics. Springer.
  • Castañeda, L. M. E., & López-Rios, V. I. (2016). Optimal population designs for discrimination between two nested nonlinear mixed effects models. Revista Ciencia En Desarrollo, 7(1), 71–81. https://doi.org/10.19053/01217488.4233
  • Chi, E. M., & Reinsel, G. C. (1989). Models for longitudinal data with random effects and AR(1) errors. Journal of the American Statistical Association, 84(406), 452–459. https://doi.org/10.1080/01621459.1989.10478790
  • Diggle, P. J. (1988). An approach to the analysis of repeated measurements. Biometrics, 44(4), 959–971. https://doi.org/10.2307/2531727
  • Fedorov, V. V., Gagnon, R. C., & Leonov, S. L. (2002). Design of experiments with unknown parameters in variance. Applied Stochastic Models in Business and Industry, 18(3), 207–218. https://doi.org/10.1002/(ISSN)1526-4025
  • Gagnon, R., & Leonov, S. (2005). Optimal population designs for PK models with serial sampling. Journal of Biopharmaceutical Statistics, 15(1), 143–163. https://doi.org/10.1081/BIP-200040853
  • Liang, K. Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 78(1), 13–22. https://doi.org/10.1093/biomet/73.1.13
  • Liu, X., Yue, R. X., & Wong, W. K. (2019). D-optimal designs for multi-response linear mixed models. Metrika, 82, 87–98. https://doi.org/10.1007/s00184-018-0679-7
  • Molenberghs, G., & Verbeke, G. (2005). Models for discrete longitudinal data. Springer Science+Business Media, Inc.
  • Nyberg, J., Höglund, R., Bergstrand, M., Karlsson, M. O., & Hooker, A. C. (2012). Serial correlation in optimal design for nonlinear mixed effects models. Journal of Pharmacokinetics and Pharmacodynamics, 39(3), 239–249. https://doi.org/10.1007/s10928-012-9245-5
  • Ogungbenro, K., Gueorguieva, I., Majid, O., Graham, G., & Aarons, L. (2007). Optimal design for multiresponse pharmacokinetic-pharmacodynamic models – dealing with unbalanced designs. Journal of Pharmacokinetics and Pharmacodynamic, 34(3), 313–331. https://doi.org/10.1007/s10928-006-9048-7
  • Roy, A. (2006). Estimating correlation coefficient between two variables with repeated observations using mixed effects model. Biometrical Journal, 48(2), 286–301. https://doi.org/10.1002/(ISSN)1521-4036
  • Schmelter, T. (2007a). The optimality of single-group designs for certain mixed models. Metrika, 65, 183–193. https://doi.org/10.1007/s00184-006-0068-5
  • Schmelter, T. (2007b). Considerations on group-wise identical designs for linear mixed models. Journal of Statistical Planning and Inference, 137(12), 4003–4010. https://doi.org/10.1016/j.jspi.2007.04.017
  • Tan, F. E. S., & Berger, M. P. F. (1999). Optimal allocation of time points for the random effects model. Communications in Statistics-Simulation and Computation, 28(2), 517–540. https://doi.org/10.1080/03610919908813563
  • Tekle, F. B., Tan, F. E. S., & Berger, M. P. F. (2008). D-optimal cohort designs for linear mixed-effects model. Statistics in Medicine, 27(14), 2586–2600. https://doi.org/10.1002/sim.v27:14
  • Verbeke, G., Fieuws, S., Molenberghs, G., & Davidian, M. (2014). The analysis of multivariate longitudinal data: A review. Statistical Methods in Medical Research, 23(1), 42–59. https://doi.org/10.1177/0962280212445834
  • Verbeke, G., & Molenberghs, G. (2000). Linear mixed models for longitudinal data, Springer Series in Statistics. Springer.
  • Wang, W. L., & Fan, T. H. (2010). ECM-based maximum likelihood inference for multivariate linear mixed models with autoregressive errors. Computational Statistics and Data Analysis, 54(5), 1328–1341. https://doi.org/10.1016/j.csda.2009.11.021

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.