False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments

Online controlled experiments (a.k.a. A/B testing) have been used as the mantra for data-driven decision making on feature changing and product shipping in many Internet companies. However, it is still a great challenge to systematically measure how every code or feature change impacts millions of users with great heterogeneity (e.g. countries, ages, devices). The most commonly used A/B testing framework in many companies is based on Average Treatment Effect (ATE), which cannot detect the heterogeneity of treatment effect on users with different characteristics. In this paper, we propose statistical methods that can systematically and accurately identify Heterogeneous Treatment Effect (HTE) of any user cohort of interest (e.g. mobile device type, country), and determine which factors (e.g. age, gender) of users contribute to the heterogeneity of the treatment effect in an A/B test. By applying these methods on both simulation data and real-world experimentation data, we show how they work robustly with controlled low False Discover Rate (FDR), and at the same time, provides us with useful insights about the heterogeneity of identified user groups. We have deployed a toolkit based on these methods, and have used it to measure the Heterogeneous Treatment Effect of many A/B tests at Snap

Semilinear reaction–diffusion systems of several components

AbstractThe homogeneous Dirichlet boundary value problemuit−Δui=∏j=1nujpij,i=1,2,…,nin a bounded domain Ω⊂RN is considered, where pij⩾0(1⩽i,j⩽n) are constants. Denote by I the identity matrix and P=(pij), which is assumed to be irreducible. We find out that whether or not I−P is a so-called M-matrix plays a fundamental role in the blow-up theorems

Sure Screening for Transelliptical Graphical Models

We propose a sure screening approach for recovering the structure of a transelliptical graphical model in the high dimensional setting. We estimate the partial correlation graph by thresholding the elements of an estimator of the sample correlation matrix obtained using Kendall's tau statistic. Under a simple assumption on the relationship between the correlation and partial correlation graphs, we show that with high probability, the estimated edge set contains the true edge set, and the size of the estimated edge set is controlled. We develop a threshold value that allows for control of the expected false positive rate. In simulation and on an equities data set, we show that transelliptical graphical sure screening performs quite competitively with more computationally demanding techniques for graph estimation.Comment: The paper won the David Byar travel award in the Joint Statistical Meetings (JSM) 201

Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net: a cohort study.

BackgroundThe capacity of electronic health record (EHR) data to guide targeted surveillance in chronic kidney disease (CKD) is unclear. We sought to leverage EHR data for predicting risk of progressing from CKD to end-stage renal disease (ESRD) to help inform surveillance of CKD among vulnerable patients from the healthcare safety-net.MethodsWe conducted a retrospective cohort study of adults (n = 28,779) with CKD who received care within 2 regional safety-net health systems during 1996-2009 in the Western United States. The primary outcomes were progression to ESRD and death as ascertained by linkage with United States Renal Data System and Social Security Administration Death Master files, respectively, through September 29, 2011. We evaluated the performance of 3 models which included demographic, comorbidity and laboratory data to predict progression of CKD to ESRD in conditions commonly targeted for disease management (hypertension, diabetes, chronic viral diseases and severe CKD) using traditional discriminatory criteria (AUC) and recent criteria intended to guide population health management strategies.ResultsOverall, 1730 persons progressed to end-stage renal disease and 7628 died during median follow-up of 6.6 years. Performance of risk models incorporating common EHR variables was highest in hypertension, intermediate in diabetes and chronic viral diseases, and lowest in severe CKD. Surveillance of persons who were in the highest quintile of ESRD risk yielded 83-94 %, 74-95 %, and 75-82 % of cases who progressed to ESRD among patients with hypertension, diabetes and chronic viral diseases, respectively. Similar surveillance yielded 42-71 % of ESRD cases among those with severe CKD. Discrimination in all conditions was universally high (AUC ≥0.80) when evaluated using traditional criteria.ConclusionsRecently proposed discriminatory criteria account for varying risk distribution and when applied to common clinical conditions may help to inform surveillance of CKD in diverse populations

A Lipoprotein Lipase–Promoting Agent, NO-1886, Improves Glucose and Lipid Metabolism in High Fat, High Sucrose–Fed New Zealand White Rabbits

The synthetic compound NO-1886 is a lipoprotein lipase activator that lowers plasma triglycerides and elevates high-density lipoprotein cholesterol (HDL-C). Recently, the authors found that NO-1886 also had an action of reducing plasma glucose in high-fat/high-sucrose diet–induced diabetic rabbits. In the current study, we investigated the effects of NO-1886 on insulin resistance and β-cell function in rabbits. Our results showed that high-fat/high-sucrose feeding increased plasma triglyceride, free fatty acid (FFA), and glucose levels and decreased HDL-C level. This diet also induced insulin resistance and impairment of acute insulin response to glucose loading. Supplementing 1% NO-1886 into the high-fat/high-sucrose diet resulted in decreased plasma triglyceride, FFA, and glucose levels and increased HDL-C level. The authors also found a clear increased glucose clearance and a protected acute insulin response to intravenous glucose loading by NO-1886 supplementation. These data suggest that NO-1886 suppresses the elevation of blood glucose in rabbits induced by feeding a high-fat/high-sucrose diet, probably through controlling lipid metabolism and improving insulin resistance

An efficient three-dimensional non-hydrostatic model for undular bores in open channels

A three-dimensional (3D) non-hydrostatic model is presented to simulate open-channel free-surface flows involving undular bores. The 3D unsteady mass conservation and momentum equations are solved using an explicit projection method in a nonstandard staggered grid. The grid system is built from a two-dimensional horizontal structured grid by adding horizontal layers. The model is validated using four typical benchmark problems, including undular bore development, an undular bore generated by a sudden discharge, and two test cases involving undular hydraulic jumps. The proposed model results are compared with experimental data and results from other models. Overall, the agreement between the proposed model results and experimental data is generally good, demonstrating the capability of the model to resolve undular bores. In addition, the non-hydrostatic pressure field under the undular free surface is revealed, and the efficiency of the proposed model is presented. It is shown that the proposed model behaves better than a volume of fluid model in terms of efficiency, because the proposed model can use fewer computational grid cells to resolve undular bores in open channels