Staff profile

About me:

I am an Associate Professor of Statistics at Durham University. Previously, I had academic positions at Imperial College, Manchester and KU Leuven. My research mainly focuses on high dimensional statistics, longitudinal data analysis, change point analysis for high dimensional data, mixed-effects models, survival analysis, nonparametric and semiparametric models, joint modelling and model diagnostics. A major focus of my research has been on developing novel statistical methods and models for the analysis of complex data such as high dimensional data and longitudinal/multilevel data, especially from medical and health studies.

I serve as Associate Editor for "Statistics & Probability Letters" journal.

I have achieved Fellowship of the Higher Education Academy. Last academic year I taught the MSc course "Models and Methods for Health Data Science". This year I teach the course "High Dimensional Statistics" for final year maths and statistics students.

If you are interested to do a PhD in Statistics or Data Science, please get in touch.

• High dimensional statistics
• Longitudinal data analysis
• Change point analysis for high dimensional data
• Statistical modelling and inference
• Biostatistics
• Survival analysis

Conference Paper

• Zhang, L., & Drikvandi, R. (2023). High dimensional change points: challenges and some proposals. . https://doi.org/10.11159/icsta23.142
• Drikvandi, R. (2021). Diagnostic tools for random effects in general mixed models.
• Drikvandi, R. (2020). Invited session "Recent advances in biostatistics".
• Drikvandi, R. (2019). A novel method for analysis of high dimensional data.
• Drikvandi, R. (2019). Joint modelling of longitudinal data involving time-varying covariates.
• Drikvandi, R. (2017). A joint mixed model for longitudinal data involving time-varying covariates.
• Drikvandi, R. (2016). A joint semiparametric mixed model for longitudinal data involving time-varying covariates.
• Drikvandi, R. (2015). Assessing the random effects part of mixed models.
• Drikvandi, R. (2014). Diagnosing misspecication of the random-effects distribution in mixed models.

Journal Article

• Al-Zubaidy, N., Crespo, R., Jones, S., Drikvandi, R., Gould, L., Leis, M., …Darzi, A. Using big data analytics to explore the relationship between government stringency and preventative social behaviour during the COVID-19 pandemic in the United Kingdom [Preprint]
• Drikvandi, R., Verbeke, G., & Molenberghs, G. (2024). A framework for analysing longitudinal data involving time-varying covariates. Annals of Applied Statistics, 18(2), 1618-1641. https://doi.org/10.1214/23-AOAS1851
• Al-Zubaidy, N., Fernandez Crespo, R., Jones, S., Gould, L., Leis, M., Maheswaran, H., …Drikvandi, R. (2023). Exploring the relationship between government stringency and preventative social behaviours during the COVID-19 pandemic in the United Kingdom. Health Informatics Journal, 29(4), Article 14604582231215867. https://doi.org/10.1177/14604582231215867
• Drikvandi, R., & Lawal, O. (2023). Sparse principal component analysis for natural language processing. Annals of Data Science, 10(1), 25-41. https://doi.org/10.1007/s40745-020-00277-x
• Rubio, J., & Drikvandi, R. (2022). MEGH: A parametric class of general hazard models for clustered survival data. Statistical Methods in Medical Research, 31(8), 1603-1616. https://doi.org/10.1177/09622802221102620
• Drikvandi, R. (2020). Nonlinear mixed-effects models with misspecified random-effects distribution. Pharmaceutical Statistics, 19(3), 187-201. https://doi.org/10.1002/pst.1981
• Sirimongkolkasem, T., & Drikvandi, R. (2019). On regularisation methods for analysis of high dimensional data. Annals of Data Science, 6(4), 737-763. https://doi.org/10.1007/s40745-019-00209-4
• Rao, K., Drikvandi, R., & Saville, B. (2019). Permutation and Bayesian tests for testing random effects in linear mixed-effects models. Statistics in Medicine, 38(25), 5034-5047. https://doi.org/10.1002/sim.8350
• Drikvandi, R., & Noorian, S. (2019). Testing random effects in linear mixed-effects models with serially correlated errors. Biometrical Journal, 61(4), 802-812. https://doi.org/10.1002/bimj.201700203
• Drikvandi, R. (2017). Nonlinear mixed-effects models for pharmacokinetic data analysis: assessment of the random-effects distribution. Journal of Pharmacokinetics and Pharmacodynamics, 44(3), 223-232. https://doi.org/10.1007/s10928-017-9510-8
• Efendi, A., Drikvandi, R., Verbeke, G., & Molenberghs, G. (2017). A goodness-of-fit test for the random-effects distribution in mixed models. Statistical Methods in Medical Research, 26(2), 970-983. https://doi.org/10.1177/0962280214564721
• Drikvandi, R., Verbeke, G., & Molenberghs, G. (2017). Diagnosing misspecification of the random-effects distribution in mixed models. Biometrics, 73(1), 63-71. https://doi.org/10.1111/biom.12551
• Drikvandi, R., Verbeke, G., & Molenberghs, G. (2017). Supplementary materials for: Diagnosing misspecification of the random-effects distribution in mixed models. Biometrics, https://doi.org/10.1111/biom.12551
• Drikvandi, R., Verbeke, G., Khodadadi, A., & Partovi Nia, V. (2013). Testing multiple variance components in linear mixed-effects models. Biostatistics, 14(1), 144-159. https://doi.org/10.1093/biostatistics/kxs028
• Drikvandi, R., Khodadadi, A., & Verbeke, G. (2012). Testing variance components in balanced linear growth curve models. Journal of Applied Statistics, 39(3), 563-572. https://doi.org/10.1080/02664763.2011.603294
• Drikvandi, R., Modarres, R., & Jalilian, A. H. (2011). A bootstrap test for symmetry based on ranked set samples. Computational Statistics & Data Analysis, 55(4), 1807-1814. https://doi.org/10.1016/j.csda.2010.11.012

Report

• Drikvandi, R., Williams, A., Boustati, A., Ezer, D., Arenas, D., de Wiljes, J., …Ceritli, T. (2018). CodeCheck: How do our food choices affect climate change?. [No known commissioning body]