Learning Treatment-Response Models from Multivariate Longitudinal Data

By Hossein Soleimani, Adarsh Subbaswamy, Suchi Saria. 2017

21 citations recorded by the source corpus.

Treatment effects can be estimated from observational data as the difference in potential outcomes. In this paper, we address the challenge of estimating the potential outcome when treatment-dose levels can vary continuously over time. Further, the outcome variable may not be measured at a regular frequency. Our proposed solution represents the treatment response curves using linear time-invariant dynamical systems---this provides a flexible means for modeling response over time to highly variable dose curves. Moreover, for multivariate data, the proposed method: uncovers shared structure in treatment response and the baseline across multiple markers; and, flexibly models challenging correlation structure both across and within signals over time. For this, we build upon the framework of multiple-output Gaussian Processes. On simulated and a challenging clinical dataset, we show significant gains in accuracy over state-of-the-art models.

Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “Soleimani et al. (2017) extend the approach in Xu et al. (2016) to the continuous time-setting and model treatment responses using linear time-invariant dynamical systems.”
From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges (2020, background): “Consequently, such methods are not capable of handling complex nonlinear relationships and long-term dependencies among the history of patient covariates, treatments, and potential outcomes.”