Group regularization for zero-inflated negative binomial regression models with an application to health care demand in Germany

Human disease clinical treatment network for the elderly: analysis of the medicare inpatient length of stay and readmission data

Clinical treatment outcomes are the quality and cost targets that health-care providers aim to improve. Most existing outcome analysis focuses on a single disease or all diseases combined. Motivated by the success of molecular and phenotypic human disease networks (HDNs), this article develops a clinical treatment network that describes the interconnections among diseases in terms of inpatient length of stay (LOS) and readmission. Here one node represents one disease, and two nodes are linked with an edge if their LOS and number of readmissions are conditionally dependent. This is the very first HDN that jointly analyzes multiple clinical treatment outcomes at the pan-disease level. To accommodate the unique data characteristics, we propose a modeling approach based on two-part generalized linear models and estimation based on penalized integrative analysis. Analysis is conducted on the Medicare inpatient data of 100,000 randomly selected subjects for the period of January 2010 to December 2018. The resulted network has 1008 edges for 106 nodes. We analyze key network properties including connectivity, module/hub, and temporal variation. The findings are biomedically sensible. For example, high connectivity and hub conditions, such as disorders of lipid metabolism and essential hypertension, are identified. There are also findings that are less/not investigated in the literature. Overall, this study can provide additional insight into diseases' properties and their interconnections and assist more efficient disease management and health-care resources allocation.

Sparse group regularization for semi-continuous transportation data

Motor vehicle crashes are a global public health concern. Most analysis have used zero-inflated count models for examining crash counts. However, few methods are available to account for safety metrics that have semi-continuous observations. This article considers the problem of variable selection for the semi-continuous zero-inflated (SCZI) models. These models include two parts: a zero-inflated part and a nonzero continuous part. A special group regularization is designed to accommodate the unique structure of two-part SCZI models, and a type of Bayesian information criterion is proposed to select tuning parameters. We illustrate the variable selection process of the proposed model using lane position data from a driving simulator study. In the study, drivers stay in the intended lane for the majority of their drive (zero-inflated part). On occasion, some drivers do drift out of their intended driving lane (nonzero continuous part). Our findings show that individual differences can be captured with the proposed model, which has implications for driving safety and the design of in-vehicle alerting systems.

A Note on the Adaptive LASSO for Zero-Inflated Poisson Regression

We consider the problem of modelling count data with excess zeros using Zero-Inflated Poisson (ZIP) regression. Recently, various regularization methods have been developed for variable selection in ZIP models. Among these, EM LASSO is a popular method for simultaneous variable selection and parameter estimation. However, EM LASSO suffers from estimation inefficiency and selection inconsistency. To remedy these problems, we propose a set of EM adaptive LASSO methods using a variety of data-adaptive weights. We show theoretically that the new methods are able to identify the true model consistently, and the resulting estimators can be as efficient as oracle. The methods are further evaluated through extensive synthetic experiments and applied to a German health care demand dataset.