Arielle Anderer

Arielle Anderer
  • Doctoral Candidate

Contact Information

  • office Address:

    533.6 Jon M. Huntsman Hall
    3730 Walnut Street
    Philadelphia, PA 19104

Research Interests: Healthcare Operations, Clinical Trial Design, Multi-armed Bandits, Transfer Learning

Links: CV, Personal Website


Arielle Anderer is a fifth year PhD student in the Operations, Information, and Decisions Department¬†at the University of Pennsylvania’s Wharton School. Her research is centered around studying adaptive learning algorithms for healthcare operations. Her work thus far has been focused on applications to the design of clinical trials and disease pre-screening, but has broader applications to other areas as well.

Continue Reading


  • Arielle Anderer, Hamsa Bastani, John Silberholz (2022), Adaptive Clinical Trial Designs with Surrogates: When Should We Bother?, Management Science (2022).

    Abstract: The success of a new drug is assessed within a clinical trial using a primary endpoint, which is typically the true outcome of interest, e.g., overall survival. However, regulators sometimes approve drugs using a surrogate outcome --- an intermediate indicator that is faster or easier to measure than the true outcome of interest, e.g., progression-free survival --- as the primary endpoint when there is demonstrable medical need. While using a surrogate outcome (instead of the true outcome) as the primary endpoint can substantially speed up clinical trials and lower costs, it can also result in poor drug approval decisions since the surrogate is not a perfect predictor of the true outcome. In this paper, we propose combining data from both surrogate and true outcomes to improve decision-making within a late-phase clinical trial. In contrast to broadly used clinical trial designs that rely on a single primary endpoint, we propose a Bayesian adaptive clinical trial design that simultaneously leverages both observed outcomes to inform trial decisions. We perform comparative statics on the relative benefit of our approach, illustrating the types of diseases and surrogates for which our proposed design is particularly advantageous. Finally, we illustrate our proposed design on metastatic breast cancer. We use a large-scale clinical trial database to construct a Bayesian prior, and simulate our design on a subset of clinical trials. We estimate that our design would yield a 16% decrease in trial costs relative to existing clinical trial designs, while maintaining the same Type I/II error rates.

Awards and Honors