Email chandrap@nus.edu.sg

I am a statistician experienced in collaborative work alongside clinicians and health economists. My expertise spans Bayesian methods, handling censored/incomplete data, managing competing risks, and analyzing longitudinal data.

My research centers on harnessing comprehensive electronic health records, large-scale observational studies, and clinical trials. Through these avenues, I investigate real-world healthcare service utilization, evaluate the economic impact of chronic conditions, and build advanced machine learning models for predicting clinical risks and treatment outcomes. My passion lies in comparative and cost-effectiveness research, where I assess emerging healthcare technologies. My mission is to identify both cost-effective and ethically sound applications of innovative medical and policy approaches, including precision medicine and inventive reimbursement models.

Currently, I’m immersed in the realm of infectious diseases and transmission modeling. Here, I employ statistical methods to unravel the dynamics of diseases, providing valuable insights to inform public health strategies.

• NUS Saw Swee Hock School of Public Health

• Bayesian Inference
• Longitudinal Data
• Chronic Diseases
• Infectious Disease
• Competing Risks
• Censored/Incomplete Data

• Statistical Inference
• Regression Analysis
• Survival Analysis

• Doctor of Philosophy (Ph.D.) in Statistics
• Master of Science in Statistics
• Bachelor of Science with Statistics and Mathematics

• Postdoctoral Fellow, Biostatistics and Modelling Domain, Saw Swee Hock School of Public Health NUS, Sep 2023-Present
• Research Associate, Health Systems and Behavioural Sciences, Saw Swee Hock School of Public Health NUS, Aug 2022-Aug 2023

• “Survival analysis of random censoring with inverse Maxwell distribution: an
  application to guinea pigs data”, Electronic Journal of Applied Statistical Analysis, Vol. 0,
  No. 0, June 2023.
• “Individualised risk prediction model for exacerbations in patients with severe
  asthma: protocol for a multicentre real-world risk modelling study”, BMJ Open,
  BMJ, Feb 2023. http://dx.doi.org/10.1136/bmjopen-2022-070459.
• A Competing Risk Study of Menarcheal Age Distribution Based on Non-Recalled
  Current Status Data, Journal of Applied Statistics, Taylor & Francis, March 2022. https:
  //doi.org/10.1080/02664763.2022.2052821.
• Cold Environment Accompanied by High-Cholesterol and Obesity Augments SARSCoV-
  2 Infectivity, Scientific Reports, Springer Nature, March 2022. https://www.nature.
  com/articles/s41598-022-08485-6.
• Latent Growth Curve Modeling for COVID-19 Cases in Presence of Time Variant
  Covariate, Computational Intelligence and Neuroscience, Hindawi, Volume 2022, Article ID
  3538866, 11 pages. https://doi.org/10.1155/2022/3538866.
• Statistical Analysis of Marshall-Olkin inverse Maxwell Distribution: Estimation
  and Application to Real Data, Reliability Theory & Applications, No. 3 (63), Volume 16,
  September 2021. https://doi.org/10.24412/1932-2321-2021-363-249-272.