Journal of Data Science

Clinical risk prediction models are commonly developed in a post-hoc and passive fashion, capitalizing on convenient data from completed clinical trials or retrospective cohorts. Impacts of the models often end at their publication rather than with the patients. The field of clinical risk prediction is rapidly improving in a progressively more transparent data science era. Based on collective experience over the past decade by the Prostate Biopsy Collaborative Group (PBCG), this paper proposes the following four data science-driven strategies for improving clinical risk prediction to the benefit of clinical practice and research. The first proposed strategy is to actively design prospective data collection, monitoring, analysis and validation of risk tools following the same standards as for clinical trials in order to elevate the quality of training data. The second suggestion is to make risk tools and model formulas available online. User-friendly risk tools will bring quantitative information to patients and their clinicians for improved knowledge-based decision-making. As past experience testifies, online tools expedite independent validation, providing helpful information as to whether the tools are generalizable to new populations. The third proposal is to dynamically update and localize risk tools to adapt to changing demographic and clinical landscapes. The fourth strategy is to accommodate systematic missing data patterns across cohorts in order to maximize the statistical power in model training, as well as to accommodate missing information on the end-user side too, in order to maximize utility for the public.

Single-index models are becoming increasingly popular in many scientific applications as they offer the advantages of flexibility in regression modeling as well as interpretable covariate effects. In the context of survival analysis, the single-index hazards models are natural extensions of the Cox proportional hazards models. In this paper, we propose a novel estimation procedure for single-index hazard models under a monotone constraint of the index. We apply the profile likelihood method to obtain the semiparametric maximum likelihood estimator, where the novelty of the estimation procedure lies in estimating the unknown monotone link function by embedding the problem in isotonic regression with exponentially distributed random variables. The consistency of the proposed semiparametric maximum likelihood estimator is established under suitable regularity conditions. Numerical simulations are conducted to examine the finite-sample performance of the proposed method. An analysis of breast cancer data is presented for illustration.

Multiclass probability estimation is the problem of estimating conditional probabilities of a data point belonging to a class given its covariate information. It has broad applications in statistical analysis and data science. Recently a class of weighted Support Vector Machines (wSVMs) has been developed to estimate class probabilities through ensemble learning for K-class problems (Wu et al., 2010; Wang et al., 2019), where K is the number of classes. The estimators are robust and achieve high accuracy for probability estimation, but their learning is implemented through pairwise coupling, which demands polynomial time in K. In this paper, we propose two new learning schemes, the baseline learning and the One-vs-All (OVA) learning, to further improve wSVMs in terms of computational efficiency and estimation accuracy. In particular, the baseline learning has optimal computational complexity in the sense that it is linear in K. Though not the most efficient in computation, the OVA is found to have the best estimation accuracy among all the procedures under comparison. The resulting estimators are distribution-free and shown to be consistent. We further conduct extensive numerical experiments to demonstrate their finite sample performance.

Many undergraduate students who matriculated in Science, Technology, Engineering and Mathematics (STEM) degree programs drop out or switch their major. Previous studies indicate that performance of students in prerequisite courses is important for attrition of students in STEM. This study analyzed demographic information, ACT/SAT score, and performance of students in freshman year courses to develop machine learning models predicting their success in earning a bachelor’s degree in biology. The predictive model based on Random Forest (RF) and Extreme Gradient Boosting (XGBoost) showed a better performance in terms of AUC (Area Under the Curve) with more balanced sensitivity and specificity than Logistic Regression (LR), K-Nearest Neighbor (KNN), and Neural Network (NN) models. An explainable machine learning approach called break-down was employed to identify important freshman year courses that could have a larger impact on student success at the biology degree program and student levels. More important courses identified at the program level can help program coordinators to prioritize their effort in addressing student attrition while more important courses identified at the student level can help academic advisors to provide more personalized, data-driven guidance to students.

Modeling heterogeneity on heavy-tailed distributions under a regression framework is challenging, yet classical statistical methodologies usually place conditions on the distribution models to facilitate the learning procedure. However, these conditions will likely overlook the complex dependence structure between the heaviness of tails and the covariates. Moreover, data sparsity on tail regions makes the inference method less stable, leading to biased estimates for extreme-related quantities. This paper proposes a gradient boosting algorithm to estimate a functional extreme value index with heterogeneous extremes. Our proposed algorithm is a data-driven procedure capturing complex and dynamic structures in tail distributions. We also conduct extensive simulation studies to show the prediction accuracy of the proposed algorithm. In addition, we apply our method to a real-world data set to illustrate the state-dependent and time-varying properties of heavy-tail phenomena in the financial industry.

This study analyzes the impact of the COVID-19 pandemic on subjective well-being as measured through Twitter for the countries of Japan and Italy. In the first nine months of 2020, the Twitter indicators dropped by 11.7% for Italy and 8.3% for Japan compared to the last two months of 2019, and even more compared to their historical means. To understand what affected the Twitter mood so strongly, the study considers a pool of potential factors including: climate and air quality data, number of COVID-19 cases and deaths, Facebook COVID-19 and flu-like symptoms global survey data, coronavirus-related Google search data, policy intervention measures, human mobility data, macro economic variables, as well as health and stress proxy variables. This study proposes a framework to analyse and assess the relative impact of these external factors on the dynamic of Twitter mood and further implements a structural model to describe the underlying concept of subjective well-being. It turns out that prolonged mobility restrictions, flu and Covid-like symptoms, economic uncertainty and low levels of quality in social interactions have a negative impact on well-being.

Social network analysis has created a productive framework for the analysis of the histories of patient-physician interactions and physician collaboration. Notable is the construction of networks based on the data of “referral paths” – sequences of patient-specific temporally linked physician visits – in this case, culled from a large set of Medicare claims data in the United States. Network constructions depend on a range of choices regarding the underlying data. In this paper we introduce the use of a five-factor experiment that produces 80 distinct projections of the bipartite patient-physician mixing matrix to a unipartite physician network derived from the referral path data, which is further analyzed at the level of the 2,219 hospitals in the final analytic sample. We summarize the networks of physicians within a given hospital using a range of directed and undirected network features (quantities that summarize structural properties of the network such as its size, density, and reciprocity). The different projections and their underlying factors are evaluated in terms of the heterogeneity of the network features across the hospitals. We also evaluate the projections relative to their ability to improve the predictive accuracy of a model estimating a hospital’s adoption of implantable cardiac defibrillators, a novel cardiac intervention. Because it optimizes the knowledge learned about the overall and interactive effects of the factors, we anticipate that the factorial design setting for network analysis may be useful more generally as a methodological advance in network analysis.

Social determinants of health (SDOH) are the conditions in which people are born, grow, work, and live. Although evidence suggests that SDOH influence a range of health outcomes, health systems lack the infrastructure to access and act upon this information. The purpose of this manuscript is to explain the methodology that a health system used to: 1) identify and integrate publicly available SDOH data into the health systems’ Data Warehouse, 2) integrate a HIPAA compliant geocoding software (via DeGAUSS), and 3) visualize data to inform SDOH projects (via Tableau). First, authors engaged key stakeholders across the health system to convey the implications of SDOH data for our patient population and identify variables of interest. As a result, fourteen publicly available data sets, accounting for >30,800 variables representing national, state, county, and census tract information over 2016–2019, were cleaned and integrated into our Data Warehouse. To pilot the data visualization, we created county and census tract level maps for our service areas and plotted common SDOH metrics (e.g., income, education, insurance status, etc.). This practical, methodological integration of SDOH data at a large health system demonstrated feasibility. Ultimately, we will repeat this process system wide to further understand the risk burden in our patient population and improve our prediction models – allowing us to become better partners with our community.

This paper introduces the package open-crypto for free-of-charge and systematic cryptocurrency data collecting. The package supports several methods to request (1) static data, (2) real-time data and (3) historical data. It allows to retrieve data from over 100 of the most popular and liquid exchanges world-wide. New exchanges can easily be added with the help of provided templates or updated with build-in functions from the project repository. The package is available on GitHub and the Python package index (PyPi). The data is stored in a relational SQL database and therefore accessible from many different programming languages. We provide a hands-on and illustrations for each data type, explanations on the received data and also demonstrate the usability from R and Matlab. Academic research heavily relies on costly or confidential data, however, open data projects are becoming increasingly important. This project is mainly motivated to contribute to openly accessible software and free data in the cryptocurrency markets to improve transparency and reproducibility in research and any other disciplines.

We introduce the stepp packages for R and Stata that implement the subpopulation treatment effect pattern plot (STEPP) method. STEPP is a nonparametric graphical tool aimed at examining possible heterogeneous treatment effects in subpopulations defined on a continuous covariate or composite score. More pecifically, STEPP considers overlapping subpopulations defined with respect to a continuous covariate (or risk index) and it estimates a treatment effect for each subpopulation. It also produces confidence regions and tests for treatment effect heterogeneity among the subpopulations. The original method has been extended in different directions such as different survival contexts, outcome types, or more efficient procedures for identifying the overlapping subpopulations. In this paper, we also introduce a novel method to determine the number of subjects within the subpopulations by minimizing the variability of the sizes of the subpopulations generated by a specific parameter combination. We illustrate the packages using both synthetic data and publicly available data sets. The most intensive computations in R are implemented in Fortran, while the Stata version exploits the powerful Mata language.