##### Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data

Mathematical modeling and simulation have emerged as a potentially powerful, time- and cost effective approach to personalized cancer treatment. In order to predict the effect of a therapeutic regimen for an individual patient, it is necessary to initialize and to parametrize the model so to mirror exactly this patient’s tumor. I will present a comprehensive approach to model and simulate a breast tumor treated by two different chemotherapies in combination and not. In the multi-scale model we represent individual tumor and normal cells, with their cell cycle and others intracellular processes (depending on key molecular characteristics), the formation of blood vessels and their disruption, extracellular processes, as the diffusion of oxygen, drugs and important molecules (including VEGF which modulates vascular dynamics) . The model is informed by data estimated from routinely acquired measurements of the patient’s tumor, including histopathology, imaging, and molecular profiling. We implemented a computer system which simulates a cross-section of the tumor under a 12-week therapy regimen. We show how the model is able to reproduce patients from a clinical trial, both responders and not. We show by scenario simulation, that other drug regimens might have led to a different outcome. Approximate Bayesian Computation (ABC) is used to estimate some of the parameters.

Preprint: https://www.biorxiv.org/content/early/2018/07/19/371369

**Thursday, October 18, 2018**

## Eric Renault

Professor of Commerce, Organizations and Entrepreneurship, Professor of Economics, Brown University

##### Wald Tests When Restrictions Are Locally Singular

**Thursday, October 25, 2018**

##### Extracting Statistical Factors When Betas Are Time-Varying

This paper deals with identification and inference on the unobservable conditional factor space and its dimension in large unbalanced panels of asset returns. The model specification is essentially nonparametric regarding the way the loadings vary in time as functions of common shocks and individual characteristics. The number of active factors can also be time-varying as an effect of the changing macroeconomic environment. The method relies on recent proposals deploying instrumental variables in large panels with unobservable factors. It accommodates for a large dimension of the vector generating the conditioning information set by machine learning techniques.

Authors: P. Gagliardini, Università della Svizzera Italiana, Lugano, Switzerland, and SFI, and H. Ma, Università della Svizzera Italiana, Lugano, Switzerland, and SFI

**Thursday, November 8, 2018**

## Paolo Zaffaroni

Professor in Financial Econometrics, Business School, Imperial College London, UK

##### Beyond the Bound: Pricing Assets with Misspecified Stochastic Discount Factors

We show how, given a misspecified stochastic discount factor (SDF), one can con- struct an admissible SDF, namely an SDF that prices assets correctly. We characterize misspecification using the extended Arbitrage Pricing Theory (APT) developed in Raponi, Uppal and Zaffaroni (2017), which allows not just for small but also large pricing errors that are pervasive (related to factors). We show how the pricing errors implied by the extended APT can be exploited to develop a theory that provides the correction required to a given SDF in order to obtain an admissible SDF that is robust to model misspecification. We show that the corrected SDF is on the mean-variance efficient frontier, and thus satisfies the Hansen and Jagannathan (1991) bound exactly. For the case where the number of assets, N, is asymptotically large, we obtain results that are even stronger, in contrast to the existing literature that requires N to be small. For large N, we show that the component of the SDF, corresponding to the missing pervasive factors, recovers exactly the contribution of such missing factors to the admissible SDF without requiring one to identify which or how many factors are missing. Estimation of our admissible SDF does not suffer from the curse of dimensionality that typically arises when N is large because of the structure imposed by the extended APT.

Joint paper with Raman Uppal and Irina Zviadadze

##### TBA

**Thursday, November 29, 2018 – 10am to noon**

## Michael Barnett and Dachuan Chen, 2018 Stevanovich Fellows

Respectively PhD student in the joint program in Financial Economics, University of Chicago and PhD student in Business Administration, University of Illinois at Chicago.