arXiv subjects

Hamidreza Badri

Publications and source records attributed to Hamidreza Badri.

4 recordsLinked to original sources

arXiv2021

Estimating Incremental Acquisition of Content Launches in a Subscription Service

Subscription services face a difficult problem when estimating the causal impact of content launches on acquisition. Customers buy subscriptions, not individual pieces of content, and once subscribed they may consume many pieces of content in addition to the one(s) that drew them to the service. In this paper, we propose a scalable methodology to estimate the incremental acquisition impact of content launches in a subscription business model when randomized experimentation is not feasible. Our approach uses simple assumptions to transform the problem into an equivalent question: what is the expected consumption rate for new subscribers who did not join due to the content launch? We estimate this counterfactual rate using the consumption rate of new subscribers who joined just prior to launch, while making adjustments for variation related to subscriber attributes, the in-product experience, and seasonality. We then compare our counterfactual consumption to the actual rate in order to back out an acquisition estimate. Our methodology provides top-line impact estimates at the content / day / region grain. Additionally, to enable subscriber-level attribution, we present an algorithm that assigns specific individual accounts to add up to the top-line estimate. Subscriber-level attribution is derived by solving an optimization problem to minimize the number of subscribers attributed to more than one piece of content, while maximizing the average propensity to be incremental for subscribers attributed to each piece of content. Finally, in the absence of definitive ground truth, we present several validation methods which can be used to assess the plausibility of impact estimates generated by these methods.

cs.SI↗

arXiv2016

Optimizing chemoradiotherapy to target multi-site metastatic disease and tumor growth

The majority of cancer-related fatalities are due to metastatic disease. In chemoradiotherapy, chemotherapeutic agents are administered along with radiation to increase damage to the primary tumor and control systemic disease such as metastasis. This work introduces a mathematical model to obtain optimal drug and radiation protocols in a chemoradiotherapy scheduling problem with the objective of minimizing metastatic cancer cell populations at multiple potential sites while maintaining a minimum level of damage to the primary tumor site. We derive closed-form expressions for an optimal chemotherapy fractionation regimen. A dynamic programming framework is used to determine the optimal radiotherapy fractionation regimen. Results show that chemotherapeutic agents do not change the optimal radiation fractionation regimens, and vice-versa. Interestingly, we observe that regardless of radio-sensitivity parameters, hypo-fractionated schedules are optimal solutions for the radiotherapy fractionation problem. Furthermore, it is optimal to immediately start radiotherapy. However, for chemotherapy, we find that the structure of the optimal schedule depends on model parameters such as chemotherapy-induced cell-kill at primary and metastatic sites, as well as the ability of primary tumor cells to initiate successful metastasis at different body sites. We quantify the trade-off between the new and traditional objectives of minimizing the metastatic population size and maximizing the tumor control probability, respectively, for a cervical cancer case. The trade-off information indicates the potential for significant reduction in the metastatic population with minimal loss in primary tumor control.

math.OC↗

arXiv2015

Minimizing Metastatic Risk in Radiotherapy Fractionation Schedules

Metastasis is the process by which cells from a primary tumor disperse and form new tumors at distant anatomical locations. The treatment and prevention of metastatic cancer remains an extremely challenging problem. This work introduces a novel biologically motivated objective function to the radiation optimization community that takes into account metastatic risk instead of the status of the primary tumor. In this work, we consider the problem of developing fractionated irradiation schedules that minimize production of metastatic cancer cells while keeping normal tissue damage below an acceptable level. A dynamic programming framework is utilized to determine the optimal fractionation scheme. We evaluated our approach on a breast cancer case using the heart and the lung as organs-at-risk (OAR). For small tumor $α/β$ values, hypo-fractionated schedules were optimal, which is consistent with standard models. However, for relatively larger $α/β$ values, we found the type of schedule depended on various parameters such as the time when metastatic risk was evaluated, the $α/β$ values of the OARs, and the normal tissue sparing factors. Interestingly, in contrast to standard models, hypo-fractionated and semi-hypo-fractionated schedules (large initial doses with doses tapering off with time) were suggested even with large tumor $α$/$β$ values. Numerical results indicate potential for significant reduction in metastatic risk.

q-bio.TO↗

arXiv2015

Robust and probabilistic optimization of dose schedules in radiotherapy

We consider the effects of parameter uncertainty on the optimal radiation schedule in the context of the linear-quadratic model. Our interest arises from the observation that if inter-patient variations in OAR and tumor sensitivities to radiation or sparing factor of the OAR are not accounted for during radiation scheduling, the performance of the therapy may be strongly degraded or the OAR may receive a substantially larger dose than the maximum threshold. This paper proposes two radiation scheduling concepts to incorporate inter-patient variability into the scheduling optimization problem. The first approach is a robust formulation that formulates the problem as a conservative model that optimizes the worst case dose scheduling that may occur. The second method is a probabilistic approach, where the model parameters are given by a set of random variables. This formulation insures that our constraints are satisfied with a given probability, and that our objective function achieves a desired level with a stated probability. We used a transformation to reduce the resulting optimization problem to two dimensions. We showed that the optimal solution lies on the boundary of the feasible region and we used a branch and bound algorithm to find the global optimal solution. We observed that if the number of fractions in the optimal conventional schedule is the same as the robust and stochastic solutions, it is preferable to administer equal or smaller total dose. In addition if there exist more (fewer) treatment sessions in the probabilistic or robust solution compared to the conventional schedule, a reduction in total dose squared (total dose) will be expected. Finally, we performed numerical experiments in the setting of head-and-neck tumors to reveal the effect of parameter uncertainty on optimal schedules and to evaluate the sensitivity of the model to the choice of key model parameters.

q-bio.TO↗