Fixed Effects High-Dimensional Profiling Models in Low Information Context.

Profiling or evaluation of health care providers, including hospitals or dialysis facilities, involves the application of hierarchical regression models to compare each provider's performance with respect to a patient outcome, such as unplanned 30-day hospital readmission. This is achieved by comparing a specific provider's estimate of unplanned readmission rate, adjusted for patient case-mix, to a normative standard, typically defined as an "average" national readmission rate across all providers. Profiling is of national importance in the United States because the Centers for Medicare and Medicaid Services (CMS) policy for payment to providers is dependent on providers' performance, which is part of a national strategy to improve delivery and quality of patient care.

Impact of Case-Mix Measurement Error on Estimation and Inference in Profiling of Health Care Providers.

Profiling analysis aims to evaluate health care providers by modeling each provider's performance with respect to a patient outcome, such as unplanned hospital readmission. High-dimensional regression models are used in profiling to risk-adjust for patient case-mix covariates. Case-mix covariates typically ascertained from administrative databases are inherently error-prone.

Profiling dialysis facilities for adverse recurrent events.

Profiling analysis aims to evaluate health care providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. Previous profiling methods have considered binary outcomes, such as 30-day hospital readmission or mortality. For the unique population of dialysis patients, regular blood works are required to evaluate effectiveness of treatment and avoid adverse events, including dialysis inadequacy, imbalance mineral levels, and anemia among others.

Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability.

Large external data sources may be available to augment studies that collect data to address a specific research objective. In this article we consider the problem of building regression models for prediction based on individual-level data from an "internal" study while incorporating summary information from an "external" big data source. We extend the work of Chatterjee et al (2016a) by introducing an adaptive empirical Bayes shrinkage estimator that uses the external summary-level information and the internal data to trade bias with variance for protection against departures in the conditional probability distribution of the outcome given a set of covariates between the two populations.

Cardiovascular and Metabolic Toxicity of Abiraterone in Castration-resistant Prostate Cancer: Post-marketing Experience.

Introduction: Abiraterone improves survival in metastatic castration-resistant prostate cancer (mCRPC) but may result in the development or worsening of comorbid conditions. We assessed the course of these conditions in patients receiving abiraterone in clinical practice and compared outcomes with those in clinical trials.

Materials And Methods: Medical records of patients with mCRPC who started abiraterone at an academic institution between 2012 and 2015 were reviewed for emergency department (ED) visits, hospitalizations, and the development and/or worsening of key comorbid conditions while on abiraterone.

Performance Characteristics of Profiling Methods and the Impact of Inadequate Case-mix Adjustment.

Profiling or evaluation of health care providers involves the application of statistical models to compare each provider's performance with respect to a patient outcome, such as unplanned 30-day hospital readmission, adjusted for patient case-mix characteristics. The nationally adopted method is based on random effects (RE) hierarchical logistic regression models. Although RE models are sensible for modeling hierarchical data, novel high dimensional fixed effects (FE) models have been proposed which may be well-suited for the objective of identifying sub-standard performance.

Time-dynamic profiling with application to hospital readmission among patients on dialysis.

Standard profiling analysis aims to evaluate medical providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. The outcome, for instance, may be mortality, medical complications, or 30-day (unplanned) hospital readmission. Profiling analysis involves regression modeling of a patient outcome, adjusting for patient health status at baseline, and comparing each provider's outcome rate (e.

Temporal and geographic variation in the systemic treatment of advanced prostate cancer.

Background: Several systemic treatments have been shown to increase survival for patients with metastatic castration-resistant prostate cancer. This study sought to characterize variation in use of the six "focus drugs" (docetaxel, abiraterone, enzalutamide, sipuleucel-T, radium-223, and cabazitaxel) that have been approved by the Food and Drug Administration for the treatment of metastatic castration-resistant prostate cancer during the years 2010-2015. We hypothesized that the use of these treatments would vary over time and by region of the country.

Meta-analysis of gene-environment interaction exploiting gene-environment independence across multiple case-control studies.

Multiple papers have studied the use of gene-environment (G-E) independence to enhance power for testing gene-environment interaction in case-control studies. However, studies that evaluate the role of G-E independence in a meta-analysis framework are limited. In this paper, we extend the single-study empirical Bayes type shrinkage estimators proposed by Mukherjee and Chatterjee (2008) to a meta-analysis setting that adjusts for uncertainty regarding the assumption of G-E independence across studies.

Exposure enriched outcome dependent designs for longitudinal studies of gene-environment interaction.

Joint effects of genetic and environmental factors have been increasingly recognized in the development of many complex human diseases. Despite the popularity of case-control and case-only designs, longitudinal cohort studies that can capture time-varying outcome and exposure information have long been recommended for gene-environment (G × E) interactions. To date, literature on sampling designs for longitudinal studies of G × E interaction is quite limited.

A Two-step Estimation Approach for Logistic Varying Coefficient Modeling of Longitudinal Data.

Varying coefficient models are useful for modeling longitudinal data and have been extensively studied in the past decade. Motivated by commonly encountered dichotomous outcomes in medical and health cohort studies, we propose a two-step method to estimate the regression coefficient functions in a logistic varying coefficient model for a longitudinal binary outcome. The model depicts time-varying covariate effects without imposing stringent parametric assumptions.

Time-varying effect modeling with longitudinal data truncated by death: conditional models, interpretations, and inference.

Recent studies found that infection-related hospitalization was associated with increased risk of cardiovascular (CV) events, such as myocardial infarction and stroke in the dialysis population. In this work, we develop time-varying effects modeling tools in order to examine the CV outcome risk trajectories during the time periods before and after an initial infection-related hospitalization. For this, we propose partly conditional and fully conditional partially linear generalized varying coefficient models (PL-GVCMs) for modeling time-varying effects in longitudinal data with substantial follow-up truncation by death.

Cardiovascular event risk dynamics over time in older patients on dialysis: a generalized multiple-index varying coefficient model approach.

Among patients on dialysis, cardiovascular disease and infection are leading causes of hospitalization and death. Although recent studies have found that the risk of cardiovascular events is higher after an infection-related hospitalization, studies have not fully elucidated how the risk of cardiovascular events changes over time for patients on dialysis. In this work, we characterize the dynamics of cardiovascular event risk trajectories for patients on dialysis while conditioning on survival status via multiple time indices: (1) time since the start of dialysis, (2) time since the pivotal initial infection-related hospitalization, and (3) the patient's age at the start of dialysis.