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. For example, anemic events (when hemoglobin levels exceed normative range) are recurrent and common for patients on dialysis. Thus, we propose high-dimensional Poisson and negative binomial regression models for rate/count outcomes and introduce a standardized event ratio measure to compare the event rate at a specific facility relative to a chosen normative standard, typically defined as an "average" national rate across all facilities. Our proposed estimation and inference procedures overcome the challenge of high-dimensional parameters for thousands of dialysis facilities. Also, we investigate how overdispersion affects inference in the context of profiling analysis. The proposed methods are illustrated with profiling dialysis facilities for recurrent anemia events.

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. METHODS: We used Clinformatics DataMart™ Database (OptumInsight, Eden Prairie, MN), a de-identified claims database from a national insurance provider. Our sample included patients with prostate cancer who received any of the six drugs. We describe changes in usage patterns over time and geographic region of the United States via detailed descriptive statistics. We explore both patterns of first line therapy and sequence of treatments in our database. RESULTS: Our final analysis included 4275 patients with a mean age of 74 years. Docetaxel was the most commonly used first-line therapy in 2010 (97%), 2011 (66%), and 2012 (49%). Abiraterone was the most commonly used first-line therapy in 2013 (56%), 2014 (46%), and 2015 (34%). Approximately 14% of our study cohort received ≥3 of the 6 drugs throughout their disease course. There was marked geographic variation in use of each of the drugs. CONCLUSION: Variation in treatment patterns were found with respect to both time and geographic location. Prescription rates of abiraterone outpaced docetaxel as the most commonly prescribed drug after 2013 when it became widely available. However, some regions of the country still lagged behind and prescribed less than would be expected.

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.g., 30-day readmission rate) to a normative standard (e.g., national "average"). Profiling methods exist mostly for non time-varying patient outcomes. However, for patients on dialysis, a unique population which requires continuous medical care, methodologies to monitor patient outcomes continuously over time are particularly relevant. Thus, we introduce a novel time-dynamic profiling (TDP) approach to assess the time-varying 30-day readmission rate. TDP is used to estimate, for the first time, the risk-standardized time-dynamic 30-day hospital readmission rate, throughout the time period that patients are on dialysis. We develop the framework for TDP by introducing the standardized dynamic readmission ratio as a function of time and a multilevel varying coefficient model with facility-specific time-varying effects. We propose estimation and inference procedures tailored to the problem of TDP and to overcome the challenge of high-dimensional parameters when examining thousands of dialysis facilities.

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. We therefore consider designs that can prioritize a subsample of the existing cohort for retrospective genotyping on the basis of currently available outcome, exposure, and covariate data. In this work, we propose stratified sampling based on summaries of individual exposures and outcome trajectories and develop a full conditional likelihood approach for estimation that adjusts for the biased sample. We compare the performance of our proposed design and analysis with combinations of different sampling designs and estimation approaches via simulation. We observe that the full conditional likelihood provides improved estimates for the G × E interaction and joint exposure effects over uncorrected complete-case analysis, and the exposure enriched outcome trajectory dependent design outperforms other designs in terms of estimation efficiency and power for detection of the G × E interaction. We also illustrate our design and analysis using data from the Normative Aging Study, an ongoing longitudinal cohort study initiated by the Veterans Administration in 1963. Copyright © 2017 John Wiley & Sons, Ltd.

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. We use the retrospective likelihood framework to derive an adaptive combination of estimators obtained under the constrained model (assuming G-E independence) and unconstrained model (without assumptions of G-E independence) with weights determined by measures of G-E association derived from multiple studies. Our simulation studies indicate that this newly proposed estimator has improved average performance across different simulation scenarios than the standard alternative of using inverse variance (covariance) weighted estimators that combines study-specific constrained, unconstrained, or empirical Bayes estimators. The results are illustrated by meta-analyzing 6 different studies of type 2 diabetes investigating interactions between genetic markers on the obesity related FTO gene and environmental factors body mass index and age.

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. Unconditional models that implicitly target an immortal population is not a relevant target of inference in applications involving a population with high mortality, like the dialysis population. A partly conditional model characterizes the outcome trajectory for the dynamic cohort of survivors, where each point in the longitudinal trajectory represents a snapshot of the population relationships among subjects who are alive at that time point. In contrast, a fully conditional approach models the time-varying effects of the population stratified by the actual time of death, where the mean response characterizes individual trends in each cohort stratum. We compare and contrast partly and fully conditional PL-GVCMs in our aforementioned application using hospitalization data from the United States Renal Data System. For inference, we develop generalized likelihood ratio tests. Simulation studies examine the efficacy of estimation and inference procedures.

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. The proposed estimation is simple and can be conveniently implemented using existing statistical packages such as SAS and R. We study asymptotic properties of the proposed estimators which lead to asymptotic inference and also develop bootstrap inferential procedures to test whether the coefficient functions are indeed time-varying or are equal to zero. The proposed methodology is illustrated with the analysis of a smoking cessation data set. Simulations are used to evaluate the performance of the proposed method compared to an alternative estimation method based on local maximum likelihood.

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. This is achieved by using a new class of generalized multiple-index varying coefficient (GM-IVC) models. The proposed GM-IVC models utilize a multiplicative structure and one-dimensional varying coefficient functions along each time and age index to capture the cardiovascular risk dynamics before and after the initial infection-related hospitalization among the dynamic cohort of survivors. We develop a two-step estimation procedure for the GM-IVC models based on local maximum likelihood. We report new insights on the dynamics of cardiovascular events risk using the United States Renal Data System database, which collects data on nearly all patients with end-stage renal disease in the United States. Finally, simulation studies assess the performance of the proposed estimation procedures.

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. Novel high dimensional fixed effects (FE) models have been proposed for profiling dialysis facilities and are more focused towards inference on the tail of the distribution of provider outcomes, which is well-suited for the objective of identifying sub-standard ("extreme") performance. However, the extent to which estimation and inference procedures for FE profiling models are effective when the outcome is sparse and/or when there are relatively few patients within a provider, referred to as the "low information" context, have not been examined. This scenario is common in practice when the patient outcome of interest is cause-specific 30-day readmissions, such as 30-day readmission due to infections in patients on dialysis, which is only about ~ 8% compared to the > 30% for all-cause 30-day readmission. Thus, we examine the feasibility and effectiveness of profiling models under the low information context in simulation studies and propose a novel correction method to FE profiling models to better handle sparse outcome data.

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. We examine the impact of case-mix measurement error (ME) on profiling models. The results show that even though the models' coefficient estimates are biased, this does not affect the estimation of standardized readmission ratio (SRR). However, ME leads to increased variation in SRR estimates and degrades the ability to identify under-performing providers.

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. Patient characteristics and adverse events were compared with those from clinical trials. RESULTS: In a cohort of 174 patients, 28% experienced either the development or worsening of a comorbid disease; 8% required an ED visit or hospitalization owing to known adverse effects of abiraterone. Increasing age (odds ratio [OR], 1.08; 95% confidence interval [CI], 1.01-1.17), higher prostate-specific antigen at initiation of treatment (OR, 1.68; 95% CI, 1.18-2.47), preexisting uncontrolled hypertension (OR, 7.61; 95% CI, 1.22-38.70), congestive heart failure (OR, 7.61; 95% CI, 1.22-38.70), and cardiac arrhythmias (OR, 4.73; 95% CI, 1.39-15.12) were associated with increased odds of an ED visit or hospitalization owing to known adverse effects of abiraterone. The rate of discontinuation (6%) was similar to 1 phase III trial that demonstrated the drug's efficacy in chemotherapy-naive patients. CONCLUSION: Few patients discontinued abiraterone owing to toxicity; however, the fact that over one-quarter of patients experienced the development or worsening of cardiovascular and metabolic diseases warrants development of team-based approaches to the management of these comorbid conditions.

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. However, there are limited comparative studies. Thus, we examine their relative performance, including the impact of inadequate case-mix adjustment.

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. We use simulation studies and a real data application using external summary information from the Prostate Cancer Prevention Trial to assess the performance of the proposed methods in contrast to maximum likelihood estimation and the constrained maximum likelihood (CML) method developed by Chatterjee et al (2016a). Our simulation studies show that the CML method can be biased and inefficient when the assumption of a transportable covariate distribution between the external and internal populations is violated, and our empirical Bayes estimator provides protection against bias and loss of efficiency.