Biostatistics

The biostatistics group has a pivotal role in sustaining and improving the quality of the quantitative research that is done at OUCRU. We collaborate with all the other OUCRU research groups as well as with the clinical trials unit in all stages of the study. We advise on study conception, study design, and data collection; we advise on analysis of the data or perform the data analysis ourselves, and we give a critical appraisal of how to interpret and communicate results. Besides, we enhance the statistical skills of our fellow researchers via both basic and advanced courses on statistical methods. Furthermore, we perform methodological research on statistical methods.

Our researchers

  • Lam Phung Khanh: post-doc, mostly working on dengue
  • Nhat Le Thanh Hoang: post-doc, general statistical advisor
  • Trinh Dong Huu Khanh: PhD student, working on tuberculous meningitis
  • Nguyet Nguyen Thi Minh: research assistant, general statistical advisor
  • Vuong Nguyen Lam: PhD student, working on dengue
  • Duc Du Hong: post-doc, mostly working on the VITAL project
  • Ronald B. Geskus: group head, associate professor University of Oxford

Some specific research projects

  • The construction and validation of models for diagnosis and prognosis. We combine patient characteristics with clinical and laboratory examinations in order to improve the diagnosis of the infectious agent as well as the prediction of disease outcome. Our focus is on statistical regression models rather than machine learning algorithms. We have built models to diagnose patients with symptoms that are suggestive of tuberculous meningitis (TBM) and dengue. And we have built models that identify individuals with a high risk of progressing to outcome in dengue patients or mortality in TBM patients. Results from such models can be used to adapt clinical management to the individual patient. For TBM we developed two user-friendly web calculators to use the models in clinical practice (https://thaole.shinyapps.io/tbmapp/ and https://thaole.shinyapps.io/DynamicTBMApp/). In a more theoretical study, we evaluated how best to deal with missing data in prediction models.
  • The trajectory of markers and their role in disease progression. Often the primary interest is in a clinical outcome like severe disease, death, or cure. Markers of disease progression provide important intermediate information. They may even serve as surrogate study outcomes if the clinical outcome needs too long follow-up to observe. We study the role of RNA and platelet count in dengue patients, fungal counts in patients with cryptococcal meningitis, and plasma sodium level in patients with tuberculous meningitis. Because they reflect disease progression, such markers typically vary over time within individuals. We use statistical models for a proper description of their trajectories and their relation with the clinical outcome.
  • The SARS-CoV-2 pandemic. Vietnam is in a quite unique position with respect to its response to the pandemic. The government implemented a policy of intensive contact tracing and testing, which resulted in relatively few infections and detailed data on community transmissions. We use these data to estimate important parameters from the early stage of the infection: the latency time (time from infection to becoming infectious) and the incubation time (time from infection to development of symptoms). We also analyze the impact of containment measures (such as the partial lockdown in April 2020) on the vaccination rates.

Publications with important contributions from members of the
biostatistics group

Le Thi Phuong Thao, Marcel Wolbers, A Dorothee Heemskerk et al. (2020). Dynamic Prediction of Death in Patients with Tuberculous Meningitis Using Time-updated Glasgow Coma Scale and Plasma Sodium Measurements. Clinical Infectious Diseases Volume 70, Issue 5, Pages 827–834, https://doi.org/10.1093/cid/ciz262

Thao LTP, Geskus R. (2019) A comparison of model selection methods for prediction in the presence of multiply imputed data. Biometrical Journal. Mar;61(2):343-356. https://doi.org/10.1002/bimj.201700232.

Beardsley J, Nhat LTH, Kibengo FM, et al. (2019) Do intracerebral cytokine responses explain the harmful effects of dexamethasone in human immunodeficiency virus–associated cryptococcal meningitis? Clinical Infectious Diseases; 68(9):1494–501.
https://doi.org/10.1093/cid/ciy725

Nguyen Lam Vuong, Nguyen Than Ha Quyen, Nguyen Thi Hanh Tien et al. (2021), Higher Plasma Viremia in the Febrile Phase is Associated with Adverse Dengue Outcomes Irrespective of Infecting Serotype or Host Immune Status: An Analysis of 5642 Vietnamese Cases, Clinical Infectious Diseases; https://doi.org/10.1093/cid/ciaa1840

 

Phung Khanh Lam, Tran Van Ngoc, Tran Thi Thu Thuy, et al. (2017). The value of daily platelet counts for predicting dengue shock syndrome: Results from a prospective observational study of 2301 Vietnamese children with dengue.Plos Negl Trop Dis; 11(4).