Machine Learning Methods for Causal Inference with Observational Biomedical Data

Causal inference -- the process of drawing a conclusion about the impact of an exposure on an outcome -- is foundational to biomedicine, where it is used to guide intervention. The current gold-standard approach for causal inference is randomized experimentation, such as randomized controlled trials (RCTs). Yet, randomized experiments, including RCTs, often enforce strict eligibility criteria that impede the generalizability of causal knowledge to the real world. Observational data, such as the electronic health record (EHR), is often regarded as a more representative source from which to generate causal knowledge. However, observational data is non-randomized, and therefore causal estimates from this source are susceptible to bias from confounders. This weakness complicates two central tasks of causal inference: the replication or evaluation of existing causal knowledge and the generation of new causal knowledge. In this dissertation I (i) address the feasibility of observational data to replicate existing causal knowledge and (ii) present new methods for the generation of causal knowledge with observational data, with a focus on the causal tasks of comparing an outcome between two cohorts and the estimation of attributable risks of exposures in a causal system

Characterizing non-heroin opioid overdoses using electronic health records.

Introduction: The opioid epidemic is a modern public health emergency. Common interventions to alleviate the opioid epidemic aim to discourage excessive prescription of opioids. However, these methods often take place over large municipal areas (state-level) and may fail to address the diversity that exists within each opioid case (individual-level). An intervention to combat the opioid epidemic that takes place at the individual-level would be preferable. Methods: This research leverages computational tools and methods to characterize the opioid epidemic at the individual-level using the electronic health record data from a large, academic medical center. To better understand the characteristics of patients with opioid use disorder (OUD) we leveraged a self-controlled analysis to compare the healthcare encounters before and after an individual\u27s first overdose event recorded within the data. We further contrast these patients with matched, non-OUD controls to demonstrate the unique qualities of the OUD cohort. Results: Our research confirms that the rate of opioid overdoses in our hospital significantly increased between 2006 and 2015 (P \u3c 0.001), at an average rate of 9% per year. We further found that the period just prior to the first overdose is marked by conditions of pain or malignancy, which may suggest that overdose stems from pharmaceutical opioids prescribed for these conditions. Conclusions: Informatics-based methodologies, like those presented here, may play a role in better understanding those individuals who suffer from opioid dependency and overdose, and may lead to future research and interventions that could successfully prevent morbidity and mortality associated with this epidemic

NOTCH3 p.Arg1231Cys is markedly enriched in South Asians and associated with stroke

Acknowledgements: Supported by Regeneron Pharmaceuticals, Inc. This research has been conducted using the UK Biobank Resource (project 26041). The authors thank everyone who made this work possible, particularly the UK Biobank team, their funders, the professionals from the member institutions who contributed to and supported this work, and most especially the UK Biobank participants, without whom this research would not be possible. The exome sequencing was funded by the UK Biobank Exome Sequencing Consortium (Bristol Myers Squibb, Regeneron, Biogen, Takeda, Abbvie, Alnylam, AstraZeneca and Pfizer). Ethical approval for the UK Biobank was previously obtained from the North West Center for Research Ethics Committee (11/NW/0382). Disclosure forms provided by the authors are available with the full text of this article.The genetic factors of stroke in South Asians are largely unexplored. Exome-wide sequencing and association analysis (ExWAS) in 75 K Pakistanis identified NM_000435.3(NOTCH3):c.3691 C > T, encoding the missense amino acid substitution p.Arg1231Cys, enriched in South Asians (alternate allele frequency = 0.58% compared to 0.019% in Western Europeans), and associated with subcortical hemorrhagic stroke [odds ratio (OR) = 3.39, 95% confidence interval (CI) = [2.26, 5.10], p = 3.87 × 10−9), and all strokes (OR [CI] = 2.30 [1.77, 3.01], p = 7.79 × 10−10). NOTCH3 p.Arg231Cys was strongly associated with white matter hyperintensity on MRI in United Kingdom Biobank (UKB) participants (effect [95% CI] in SD units = 1.1 [0.61, 1.5], p = 3.0 × 10−6). The variant is attributable for approximately 2.0% of hemorrhagic strokes and 1.1% of all strokes in South Asians. These findings highlight the value of diversity in genetic studies and have major implications for genomic medicine and therapeutic development in South Asian populations