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Drug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The "count" indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies. [ABSTRACT FROM AUTHOR]

Background: Red blood cells are essential for modern medicine but managing their collection and supply to cope with fluctuating demands represents a major challenge. As deterministic models based on predicted population changes have been problematic, there remains a need for more precise and reliable prediction of use. Here, we develop three new time-series methods to predict red cell use 4 to 52 weeks ahead.Study Design and Methods: From daily aggregates of red blood cell (RBC) units issued from 2005 to 2011 from the NHS Blood and Transplant, we generated a new set of non-overlapping weekly data by summing the daily data over 7 days and derived the average blood use per week over 4-week and 52-week periods. We used three new methods for linear prediction of blood use by computing the coefficients using Minimum Mean Squared Error (MMSE) algorithm.Results: We optimized the time-window size, order of the prediction, and order of the polynomial fit for our data set. By exploiting the annual periodicity of the data, we achieved significant improvements in long-term predictions, as well as modest improvements in short-term predictions. The new methods predicted mean RBC use with a standard deviation of the percentage error of 2.5% for 4 weeks ahead and 3.4% for 52 weeks ahead.Conclusion: This paradigm allows short- and long-term prediction of RBC use and could provide reliable and precise prediction up to 52 weeks ahead to improve the efficiency of blood services and sufficiency of blood supply with reduced costs. [ABSTRACT FROM AUTHOR]

Objective: Develop and validate a surveillance model to identify outpatient surgical adverse events (AEs) based on previously developed electronic triggers.Data Sources: Veterans Health Administration's Corporate Data Warehouse.Study Design: Six surgical AE triggers, including postoperative emergency room visits and hospitalizations, were applied to FY2012-2014 outpatient surgeries (n = 744,355). We randomly sampled trigger-flagged and unflagged cases for nurse chart review to document AEs and measured positive predictive value (PPV) for triggers. Next, we used chart review data to iteratively estimate multilevel logistic regression models to predict the probability of an AE, starting with the six triggers and adding in patient, procedure, and facility characteristics to improve model fit. We validated the final model by applying the coefficients to FY2015 outpatient surgery data (n = 256,690) and reviewing charts for cases at high and moderate probability of an AE.Principal Findings: Of 1,730 FY2012-2014 reviewed surgeries, 350 had an AE (20 percent). The final surveillance model c-statistic was 0.81. In FY2015 surgeries with >0.8 predicted probability of an AE (n = 405, 0.15 percent), PPV was 85 percent; in surgeries with a 0.4-0.5 predicted probability of an AE, PPV was 38 percent.Conclusions: The surveillance model performed well, accurately identifying outpatient surgeries with a high probability of an AE. [ABSTRACT FROM AUTHOR]

Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinite-dimensional target parameter, such as a regression function, in the setting where data are generated sequentially by a common conditional data distribution given summary measures of the past. This setting encompasses a wide range of time-series models and, as special case, models for independent and identically distributed data. Our estimator considers a large library of candidate online estimators and uses online cross-validation to identify the algorithm with the best performance. We show that by basing estimates on the cross-validation-selected algorithm, we are asymptotically guaranteed to perform as well as the true, unknown best-performing algorithm. We provide extensions of this approach including online estimation of the optimal ensemble of candidate online estimators. We illustrate excellent performance of our methods using simulations and a real data example where we make streaming predictions of infectious disease incidence using data from a large database. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Claims databases consisting of routinely collected longitudinal records of medical expenditures are increasingly utilized for estimating expected medical costs of patients with a specific condition. Survival data of the patients of interest are usually highly censored, and observed expenditures are incomplete. In this study, we propose a survival-adjusted estimator for estimating mean lifetime costs, which integrates the product of the survival function and the mean cost function over the lifetime horizon. The survival function is estimated by a new algorithm of rolling extrapolation, aided by external information of age- and sex-matched referents simulated from national vital statistics. The mean cost function is estimated by a weighted average of mean expenditures of patients in a number of months prior to their death, of which the number could be determined by observed costs in their final months, and the weights depend on extrapolated hazards. We evaluate the performance of the proposed approach in comparison with that of a popular method using simulated data under various scenarios and 2 cohorts of intracerebral hemorrhage and ischemic stroke patients with a maximum follow-up of 13 years and conclude that our new method estimates the mean lifetime costs more accurately. [ABSTRACT FROM AUTHOR]