Marginal structural models are a popular method for estimating causal effects in the presence of time-varying exposures. In spite of their popularity, no scalable non-parametric estimator exist for marginal structural models with multi-valued and time-varying treatments. In this paper, we use machine learning together with recent developments in semiparametric efficiency theory for longitudinal studies to propose such an estimator. The proposed estimator is based on a study of the non-parametric identifying functional, including first order von-Mises expansions as well as the efficient influence function and the efficiency bound. We show conditions under which the proposed estimator is efficient, asymptotically normal, and sequentially doubly robust in the sense that it is consistent if, for each time point, either the outcome or the treatment mechanism is consistently estimated. We perform a simulation study to illustrate the properties of the estimators, and present the results of our motivating study on a COVID-19 dataset studying the impact of mobility on the cumulative number of observed cases.

Despite recent advances in algorithmic fairness, methodologies for achieving fairness with generalized linear models (GLMs) have yet to be explored in general, despite GLMs being widely used in practice. In this paper we introduce two fairness criteria for GLMs based on equalizing expected outcomes or log-likelihoods. We prove that for GLMs both criteria can be achieved via a convex penalty term based solely on the linear components of the GLM, thus permitting efficient optimization. We also derive theoretical properties for the resulting fair GLM estimator. To empirically demonstrate the efficacy of the proposed fair GLM, we compare it with other well-known fair prediction methods on an extensive set of benchmark datasets for binary classification and regression. In addition, we demonstrate that the fair GLM can generate fair predictions for a range of response variables, other than binary and continuous outcomes.

The vast preponderance of somatic mutations in a typical cancer are either extremely rare or have never been previously recorded in available databases that track somatic mutations. These constitute a hidden genome that contrasts the relatively small number of mutations that occur frequently, the properties of which have been studied in depth. Here we demonstrate that this hidden genome contains much more accurate information than common mutations for the purpose of identifying the site of origin of primary cancers in settings where this is unknown. We accomplish this using a projection-based statistical method that achieves a highly effective signal condensation, by leveraging DNA sequence and epigenetic contexts using a set of meta-features that embody the mutation contexts of rare variants throughout the genome.

By using sequencing results from a cohort of 1,054 patients with advanced lung adenocarcinomas, we developed OncoCast, a machine learning tool for survival risk stratification and biomarker identification. We found that comutations of both STK11 and KEAP1 are strong determinants of unfavorable prognosis with currently available therapies.

This novel study suggests that polymorphisms in genes involved in aging and inflammation, dopamine, myelin and cell cycle regulation, and DNA repair and response to oxidative stress may be associated with cognitive outcome in patients with brain tumors.

OncoCast R package Please find all the package’s resources on it’s github repository. An Ensemble Learning Approach for Outcome Prediction in Precision Oncology Setting Ensemble learner framework for survival outcome prediction and stratification for high dimension data.

gnomeR R package Please find all the package’s resources on it’s github repository. Introduction The gnomeR package provides a consistent framework for genetic data processing, visualization and analysis. This is primarily targeted to IMPACT datasets but can also be applied to any genomic data provided by CbioPortal.

genomeVerse: Framework for Genomic data Processing and Analysis genomeVerse is an umbrella package for genomic data processing and analysis with the genomeVerse ecosystem of R packages developed in the department of Epidemiology & Biostatistics at Memorial Sloan Kettering Cancer Center.