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Outputs (26)

A framework for analysing longitudinal data involving time-varying covariates (2024)
Journal Article
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

Standard models for longitudinal data ignore the stochastic nature of time-varying covariates and their stochastic evolution over time by treating them as fixed variables. There have been recent methods for modelling time-varying covariates; however,... Read More about A framework for analysing longitudinal data involving time-varying covariates.

Exploring the relationship between government stringency and preventative social behaviours during the COVID-19 pandemic in the United Kingdom. (2023)
Journal Article
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

We constructed a preventive social behaviours (PSB) Index using survey questions that were aligned with WHO recommendations, and used linear regression to assess the impact of reported COVID-19 deaths (RCD), people's confidence of government handling... Read More about Exploring the relationship between government stringency and preventative social behaviours during the COVID-19 pandemic in the United Kingdom..

MEGH: A parametric class of general hazard models for clustered survival data (2022)
Journal Article
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

In many applications of survival data analysis, the individuals are treated in different medical centres or belong to different clusters defined by geographical or administrative regions. The analysis of such data requires accounting for between-clus... Read More about MEGH: A parametric class of general hazard models for clustered survival data.

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] (2021)
Working Paper
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]

We evaluated the association between preventative social behaviour and government stringency. Additionally, we sought to evaluate the influence of additional factors including time, need to protect others (using the reported number of COVID-19 deaths... Read More about 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].

Sparse principal component analysis for natural language processing (2020)
Journal Article
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

High dimensional data are rapidly growing in many different disciplines, particularly in natural language processing. The analysis of natural language processing requires working with high dimensional matrices of word embeddings obtained from text da... Read More about Sparse principal component analysis for natural language processing.

Nonlinear mixed-effects models with misspecified random-effects distribution (2019)
Journal Article
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

Nonlinear mixed‐effects models are being widely used for the analysis of longitudinal data, especially from pharmaceutical research. They use random effects which are latent and unobservable variables so the random‐effects distribution is subject to... Read More about Nonlinear mixed-effects models with misspecified random-effects distribution.

Permutation and Bayesian tests for testing random effects in linear mixed-effects models (2019)
Journal Article
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

In many applications of linear mixed‐effects models to longitudinal and multilevel data especially from medical studies, it is of interest to test for the need of random effects in the model. It is known that classical tests such as the likelihood ra... Read More about Permutation and Bayesian tests for testing random effects in linear mixed-effects models.

On regularisation methods for analysis of high dimensional data (2019)
Journal Article
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

High dimensional data are rapidly growing in many domains due to the development of technological advances which helps collect data with a large number of variables to better understand a given phenomenon of interest. Particular examples appear in ge... Read More about On regularisation methods for analysis of high dimensional data.

Testing random effects in linear mixed-effects models with serially correlated errors (2019)
Journal Article
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

In linear mixed‐effects models, random effects are used to capture the heterogeneity and variability between individuals due to unmeasured covariates or unknown biological differences. Testing for the need of random effects is a nonstandard problem b... Read More about Testing random effects in linear mixed-effects models with serially correlated errors.

CodeCheck: How do our food choices affect climate change? (2018)
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]

Different approaches were proposed to predict the carbon footprint of products from the different datasets provided by CodeCheck. Multivariate linear regression and random forest regression models perform well in predicting carbon footprint, especial... Read More about CodeCheck: How do our food choices affect climate change?.

A goodness-of-fit test for the random-effects distribution in mixed models (2017)
Journal Article
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

In this paper, we develop a simple diagnostic test for the random-effects distribution in mixed models. The test is based on the gradient function, a graphical tool proposed by Verbeke and Molenberghs to check the impact of assumptions about the rand... Read More about A goodness-of-fit test for the random-effects distribution in mixed models.

Nonlinear mixed-effects models for pharmacokinetic data analysis: assessment of the random-effects distribution (2017)
Journal Article
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

Nonlinear mixed-effects models are frequently used for pharmacokinetic data analysis, and they account for inter-subject variability in pharmacokinetic parameters by incorporating subject-specific random effects into the model. The random effects are... Read More about Nonlinear mixed-effects models for pharmacokinetic data analysis: assessment of the random-effects distribution.

Diagnosing misspecification of the random-effects distribution in mixed models (2016)
Journal Article
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

It is traditionally assumed that the random effects in mixed models follow a multivariate normal distribution, making likelihood‐based inferences more feasible theoretically and computationally. However, this assumption does not necessarily hold in p... Read More about Diagnosing misspecification of the random-effects distribution in mixed models.

Testing multiple variance components in linear mixed-effects models (2012)
Journal Article
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

Testing zero variance components is one of the most challenging problems in the context of linear mixed-effects (LME) models. The usual asymptotic chi-square distribution of the likelihood ratio and score statistics under this null hypothesis is inco... Read More about Testing multiple variance components in linear mixed-effects models.

Testing variance components in balanced linear growth curve models (2011)
Journal Article
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

It is well known that the testing of zero variance components is a non-standard problem since the null hypothesis is on the boundary of the parameter space. The usual asymptotic chi-square distribution of the likelihood ratio and score statistics und... Read More about Testing variance components in balanced linear growth curve models.

A bootstrap test for symmetry based on ranked set samples (2010)
Journal Article
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

To test the hypothesis of symmetry about an unknown median we propose the maximum of a partial sum process based on ranked set samples. We discuss the properties of the test statistic and investigate a modified bootstrap ranked set sample bootstrap p... Read More about A bootstrap test for symmetry based on ranked set samples.