Arman Oganisian

Arman Oganisian

Assistant Professor of Biostatistics

Brown University

News

  • 4/10/2023: New working paper on arXiv developing semiparametric Bayesian framework for causal inference with recurrent event outcomes.
  • Upcoming Summer 2023 talks: 5/18/2023 - IRSA; 8/5/2023 - JSM Toronto; 8/1/2023 - EcoStat Tokyo.
  • 1/5/2023: I’ll be giving a talk at ICHPS on Bayesian methods for sequential treatment strategies.
  • 12/15/2022: New paper developing a hierarchical Bayesian bootstrap for causal estimation.
  • 11/15/2022: New working paper on arXiv developing semiparametric Bayesian methods for sequential treatment decisions.

Bio

I am currently an Assistant Professor of Biostatistics at Brown University’s Department of Biostatistics. My methodological research centers around developing Bayesian methods for flexible causal effect estimation with observational data. These methods blend principled causal reasoning, nonparametric Bayesian modeling, and efficient computation to build systems for data-driven decision making.

Many of my motivating applications are in oncology - e.g. Bayesian nonparametric cost and cost-efficacy estimation for endometrial cancer treatments; Developing a Bayesian bootstrap procedure for assessing treatment effect heterogeneity of photon therapy across cancer types. My current research is partially funded by a PCORI grant and focuses on developing Bayesian semiparametric methods for estimating and optimizing effects of sequential treatment strategies with applications in leukemia.

I received my PhD in Biostatistics from the University of Pennsylvania under the supervision of Jason Roy and Nandita Mitra. Here you will find a link to my CV and a selection of some past and current research, talks, etc. I sometimes blog about statistics, Bayesian methods, computation/MCMC, and other things I happen to stumble upon during research.

Interests
  • Bayesian nonparametrics
  • Causal Inference
  • Sequential decision-making
  • Health Economics
  • Oncology
Education
  • PhD in Biostatistics, 2021

    University of Pennsylvania

  • MS in Biostatistics, 2018

    University of Pennsylvania

  • BA in Quantitative Economics, 2013

    Providence College

Projects

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Collaborative Causal and Bayesian Modeling

Collaborative Causal and Bayesian Modeling

Collaborative data analysis projects using Bayesian and/or Causal methods.

Non-parametric Bayes

Non-parametric Bayes

Bayesian modeling - flexiblilty, uncertainty quantification, full posterior inference.

Bayesian Causal Inference

Bayesian Causal Inference

Shrinkage, partial pooling, nonparametrics, and sensitivity analysis via priors - just some of the value Bayesian modeling can add to causal inference.

ChiRP

ChiRP

R Package for Dirichlet Process Mixtures of zero-inflated, logistic, and linear regressions.