Biography

I am a PhD candidate in Biostatistics at the University of Michigan. I am advised by Prof. Douglas E. Schaubel. My research is mainly in the area of survival analysis with applications in the end stage renal disease setting. My dissertation research is specifically foscused on developing statistical methods for (i) analyzing clustered competing risks data and (ii) causal inference using instrumental variables for time-to-event data. I also work as a research assistant at the University of Michigan Kidney Epidemiology and Cost Center, contributing to the Chronic Kidney Disease Surveillance System and the United States Renal Data System projects. Prior to coming to Michigan, I was a Quantitative Research Analyst at Acumen, LLC and before that I was a graduate student and research assistant at Department of Pharmacy Administration, The University of Mississippi.

Here’s my CV.

• Evaluating center performance in the competing risks setting: Application to outcomes of wait-listed end-stage renal disease patients

  Details Journal Link

• Estimating State-level Awareness of Chronic Kidney Disease in the United States

  Details Journal Link

• Food Insecurity, CKD, and Subsequent ESRD in US Adults

  Details Journal Link

• Measuring Pharmacy Performance in the Area of Medication Adherence: Addressing the Issue of Risk Adjustment.

  Details Journal Link

1. Dharmarajan, S., Schaubel, D. E. “Weighted estimators of the complier average causal effect on restricted mean survival time with observed instrument-outcome confounders.” In preparation.
2. Dharmarajan, S., Schaubel, D. E. “Improving the Efficiency of the Proportional Rates Model.” In preparation. Talk
3. Dharmarajan, S., Schaubel, D. E. “A Semiparametric Mixture Component Model with Random Effects for the Analysis of Clustered Competing Risks Data.” In preparation. Talk
4. Dharmarajan, S., Schaubel, D. E. “Instrumental Variable Methods for Competing Risks Data.” In preparation.
5. Dharmarajan, S., Morgenstern, H., Bragg-Gresham, J., Gillespie, B., Powe, N., Tuot, D., Banerjee, T., Rios-Burrows, N., Rolka, D., Saydah, S., and Saran, R. “Bayesian Small Area Estimates of Prevalence and Self-reported Prevalence of Kidney Disease by United States County.” In preparation. Talk

• A semiparametric mixture component model with random effects for the analysis of clustered competing risks data

  Aug 2, 2017, Joint Statistical Meetings, 2017

• Improving the Efficiency of the Proportional Rates Model

  Mar 12, 2017, ENAR International Biometric Society Meeting, 2017

• Bayesian Small Area Estimates of Prevalence and Self-reported Prevalence of Kidney Disease by United States County

  Nov 4, 2015, American Public Health Association Annual Meeting, 2015

• Implementation of a state Medicaid prescription cap policy: Is there an impact on beneficiaries’ medical services utilization and costs?

  May 23, 2011, International Society of Pharmacoeconomics and Outcomes Research - 16th Annual International Meeting, 2011

• Risk of Hip Fractures in Elderly medicare beneficiaries: A propensity score Analysis

  May 23, 2011, International Society of Pharmacoeconomics and outcomes Research - 16th Annual International Meeting, 2011

Advanced Proabibility and Statistical Inference

• STATS 610: Statistical Inference
• STATS 611: Large Sample Theory
• BIOSTAT 680: Stochastic Processes

Regression Techniques / Machine Learning

• STATS 601: Analysis of Multivariate and Categorical Data
• BIOSTAT 682: Applied Bayesian Inference
• BIOSTAT 675: Survival Analysis

Computing

• BIOSTAT 615: Statistical Computing
• BIOSTAT 815: Advanced Computational Statistics

Advanced Statistical Methods

• BIOSTAT 870: Analysis of Repeated Measures
• BIOSTAT 880: Missing Data
• BIOSTAT 830: Stochastic Models in Survival Analysis

My average day consists of drinking 5 cups of tea and getting yelled at plenty. Here’s a picture of me making passers by at Michigan Diag stare in disbelief, scratch their heads and crack a smile. I ocassionally blog about Cricket and Basketball.