Comparison of First-Line Dual Combination Treatments in Hypertension: Real-World Evidence from Multinational Heterogeneous Cohorts.

Background and objectives: 2018 ESC/ESH Hypertension guideline recommends 2-drug combination as initial anti-hypertensive therapy. However, real-world evidence for effectiveness of recommended regimens remains limited. We aimed to compare the effectiveness of first-line anti-hypertensive treatment combining 2 out of the following classes: angiotensin-converting enzyme (ACE) inhibitors/angiotensin-receptor blocker (A), calcium channel blocker (C), and thiazide-type diuretics (D).Methods: Treatment-naïve hypertensive adults without cardiovascular disease (CVD) who initiated dual anti-hypertensive medications were identified in 5 databases from US and Korea. The patients were matched for each comparison set by large-scale propensity score matching. Primary endpoint was all-cause mortality. Myocardial infarction, heart failure, stroke, and major adverse cardiac and cerebrovascular events as a composite outcome comprised the secondary measure.Results: A total of 987,983 patients met the eligibility criteria. After matching, 222,686, 32,344, and 38,513 patients were allocated to A+C vs. A+D, C+D vs. A+C, and C+D vs. A+D comparison, respectively. There was no significant difference in the mortality during total of 1,806,077 person-years: A+C vs. A+D (hazard ratio [HR], 1.08; 95% confidence interval [CI], 0.97-1.20; p=0.127), C+D vs. A+C (HR, 0.93; 95% CI, 0.87-1.01; p=0.067), and C+D vs. A+D (HR, 1.18; 95% CI, 0.95-1.47; p=0.104). A+C was associated with a slightly higher risk of heart failure (HR, 1.09; 95% CI, 1.01-1.18; p=0.040) and stroke (HR, 1.08; 95% CI, 1.01-1.17; p=0.040) than A+D.Conclusions: There was no significant difference in mortality among A+C, A+D, and C+D combination treatment in patients without previous CVD. This finding was consistent across multi-national heterogeneous cohorts in real-world practice

Comparison of First-Line Dual Combination Treatments in Hypertension: Real-World Evidence from Multinational Heterogeneous Cohorts

BACKGROUND AND OBJECTIVES: 2018 ESC/ESH Hypertension guideline recommends 2-drug combination as initial anti-hypertensive therapy. However, real-world evidence for effectiveness of recommended regimens remains limited. We aimed to compare the effectiveness of first-line anti-hypertensive treatment combining 2 out of the following classes: angiotensin-converting enzyme (ACE) inhibitors/angiotensin-receptor blocker (A), calcium channel blocker (C), and thiazide-type diuretics (D). METHODS: Treatment-naïve hypertensive adults without cardiovascular disease (CVD) who initiated dual anti-hypertensive medications were identified in 5 databases from US and Korea. The patients were matched for each comparison set by large-scale propensity score matching. Primary endpoint was all-cause mortality. Myocardial infarction, heart failure, stroke, and major adverse cardiac and cerebrovascular events as a composite outcome comprised the secondary measure. RESULTS: A total of 987,983 patients met the eligibility criteria. After matching, 222,686, 32,344, and 38,513 patients were allocated to A+C vs. A+D, C+D vs. A+C, and C+D vs. A+D comparison, respectively. There was no significant difference in the mortality during total of 1,806,077 person-years: A+C vs. A+D (hazard ratio [HR], 1.08; 95% confidence interval [CI], 0.97-1.20; p=0.127), C+D vs. A+C (HR, 0.93; 95% CI, 0.87-1.01; p=0.067), and C+D vs. A+D (HR, 1.18; 95% CI, 0.95-1.47; p=0.104). A+C was associated with a slightly higher risk of heart failure (HR, 1.09; 95% CI, 1.01-1.18; p=0.040) and stroke (HR, 1.08; 95% CI, 1.01-1.17; p=0.040) than A+D. CONCLUSIONS: There was no significant difference in mortality among A+C, A+D, and C+D combination treatment in patients without previous CVD. This finding was consistent across multi-national heterogeneous cohorts in real-world practice.ope

Scalable Sparse Cox's Regression for Large-Scale Survival Data via Broken Adaptive Ridge

This paper develops a new scalable sparse Cox regression tool for sparse high-dimensional massive sample size (sHDMSS) survival data. The method is a local $L_0$-penalized Cox regression via repeatedly performing reweighted $L_2$-penalized Cox regression. We show that the resulting estimator enjoys the best of $L_0$- and $L_2$-penalized Cox regressions while overcoming their limitations. Specifically, the estimator is selection consistent, oracle for parameter estimation, and possesses a grouping property for highly correlated covariates. Simulation results suggest that when the sample size is large, the proposed method with pre-specified tuning parameters has a comparable or better performance than some popular penalized regression methods. More importantly, because the method naturally enables adaptation of efficient algorithms for massive $L_2$-penalized optimization and does not require costly data driven tuning parameter selection, it has a significant computational advantage for sHDMSS data, offering an average of 5-fold speedup over its closest competitor in empirical studies

Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study

Background: Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the risk associated with its use in routine care in patients with rheumatoid arthritis. Methods: In this multinational, retrospective study, new user cohort studies in patients with rheumatoid arthritis aged 18 years or older and initiating hydroxychloroquine were compared with those initiating sulfasalazine and followed up over 30 days, with 16 severe adverse events studied. Self-controlled case series were done to further establish safety in wider populations, and included all users of hydroxychloroquine regardless of rheumatoid arthritis status or indication. Separately, severe adverse events associated with hydroxychloroquine plus azithromycin (compared with hydroxychloroquine plus amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, the Netherlands, Spain, the UK, and the USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (HRs) according to drug use. Estimates were pooled where the I2 value was less than 0·4. Findings: The study included 956 374 users of hydroxychloroquine, 310 350 users of sulfasalazine, 323 122 users of hydroxychloroquine plus azithromycin, and 351 956 users of hydroxychloroquine plus amoxicillin. No excess risk of severe adverse events was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. Self-controlled case series confirmed these findings. However, long-term use of hydroxychloroquine appeared to be associated with increased cardiovascular mortality (calibrated HR 1·65 [95% CI 1·12–2·44]). Addition of azithromycin appeared to be associated with an increased risk of 30-day cardiovascular mortality (calibrated HR 2·19 [95% CI 1·22–3·95]), chest pain or angina (1·15 [1·05–1·26]), and hear

A model not a prophet:Operationalising patient-level prediction using observational data networks

Improving prediction model developement and evaluation processes using observational health data

A model not a prophet:Operationalising patient-level prediction using observational data networks

Improving prediction model developement and evaluation processes using observational health data

High-Performance Statistical Computing in the Computing Environments of the 2020s

Technological advances in the past decade, hardware and software alike, have made access to high-performance computing (HPC) easier than ever. We review these advances from a statistical computing perspective. Cloud computing makes access to supercomputers affordable. Deep learning software libraries make programming statistical algorithms easy and enable users to write code once and run it anywhere -- from a laptop to a workstation with multiple graphics processing units (GPUs) or a supercomputer in a cloud. Highlighting how these developments benefit statisticians, we review recent optimization algorithms that are useful for high-dimensional models and can harness the power of HPC. Code snippets are provided to demonstrate the ease of programming. We also provide an easy-to-use distributed matrix data structure suitable for HPC. Employing this data structure, we illustrate various statistical applications including large-scale positron emission tomography and $\ell_1$-regularized Cox regression. Our examples easily scale up to an 8-GPU workstation and a 720-CPU-core cluster in a cloud. As a case in point, we analyze the onset of type-2 diabetes from the UK Biobank with 200,000 subjects and about 500,000 single nucleotide polymorphisms using the HPC $\ell_1$-regularized Cox regression. Fitting this half-million-variate model takes less than 45 minutes and reconfirms known associations. To our knowledge, this is the first demonstration of the feasibility of penalized regression of survival outcomes at this scale.Comment: Accepted for publication in Statistical Scienc

병렬화 용이한 통계계산 방법론과 현대 고성능 컴퓨팅 환경에의 적용

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 통계학과, 2020. 8. 원중호.Technological advances in the past decade, hardware and software alike, have made access to high-performance computing (HPC) easier than ever. In this dissertation, easily-parallelizable, inversion-free, and variable-separated algorithms and their implementation in statistical computing are discussed. The first part considers statistical estimation problems under structured sparsity posed as minimization of a sum of two or three convex functions, one of which is a composition of non-smooth and linear functions. Examples include graph-guided sparse fused lasso and overlapping group lasso. Two classes of inversion-free primal-dual algorithms are considered and unified from a perspective of monotone operator theory. From this unification, a continuum of preconditioned forward-backward operator splitting algorithms amenable to parallel and distributed computing is proposed. The unification is further exploited to introduce a continuum of accelerated algorithms on which the theoretically optimal asymptotic rate of convergence is obtained. For the second part, easy-to-use distributed matrix data structures in PyTorch and Julia are presented. They enable users to write code once and run it anywhere from a laptop to a workstation with multiple graphics processing units (GPUs) or a supercomputer in a cloud. With these data structures, various parallelizable statistical applications, including nonnegative matrix factorization, positron emission tomography, multidimensional scaling, and ℓ1-regularized Cox regression, are demonstrated. The examples scale up to an 8-GPU workstation and a 720-CPU-core cluster in a cloud. As a case in point, the onset of type-2 diabetes from the UK Biobank with 400,000 subjects and about 500,000 single nucleotide polymorphisms is analyzed using the HPC ℓ1-regularized Cox regression. Fitting a half-million variate model took about 50 minutes, reconfirming known associations. To my knowledge, the feasibility of a joint genome-wide association analysis of survival outcomes at this scale is first demonstrated.지난 10년간의 하드웨어와 소프트웨어의 기술적인 발전은 고성능 컴퓨팅의 접근장벽을 그 어느 때보다 낮추었다. 이 학위논문에서는 병렬화 용이하고 역행렬 연산이 없는 변수 분리 알고리즘과 그 통계계산에서의 구현을 논의한다. 첫 부분은 볼록 함수 두 개 또는 세 개의 합으로 나타나는 구조화된 희소 통계 추정 문제에 대해 다룬다. 이 때 함수들 중 하나는 비평활 함수와 선형 함수의 합성으로 나타난다. 그 예시로는 그래프 구조를 통해 유도되는 희소 융합 Lasso 문제와 한 변수가 여러 그룹에 속할 수 있는 그룹 Lasso 문제가 있다. 이를 풀기 위해 역행렬 연산이 없는 두 종류의 원시-쌍대 (primal-dual) 알고리즘을 단조 연산자 이론 관점에서 통합하며 이를 통해 병렬화 용이한 precondition된 전방-후방 연산자 분할 알고리즘의 집합을 제안한다. 이 통합은 점근적으로 최적 수렴률을 갖는 가속 알고리즘의 집합을 구성하는 데 활용된다. 두 번째 부분에서는 PyTorch와 Julia를 통해 사용하기 쉬운 분산 행렬 자료 구조를 제시한다. 이 구조는 사용자들이 코드를 한 번 작성하면 이것을 노트북 한 대에서부터 여러 대의 그래픽 처리 장치 (GPU)를 가진 워크스테이션, 또는 클라우드 상에 있는 슈퍼컴퓨터까지 다양한 스케일에서 실행할 수 있게 해 준다. 아울러, 이 자료 구조를 비음 행렬 분해, 양전자 단층 촬영, 다차원 척 도법, ℓ1-벌점화 Cox 회귀 분석 등 다양한 병렬화 가능한 통계적 문제에 적용한다. 이 예시들은 8대의 GPU가 있는 워크스테이션과 720개의 코어가 있는 클라우드 상의 가상 클러스터에서 확장 가능했다. 한 사례로 400,000명의 대상과 500,000개의 단일 염기 다형성 정보가 있는 UK Biobank 자료에서의 제2형 당뇨병 (T2D) 발병 나이를 ℓ1-벌점화 Cox 회귀 모형을 통해 분석했다. 500,000개의 변수가 있는 모형을 적합시키는 데 50분 가량의 시간이 걸렸으며 알려진 T2D 관련 다형성들을 재확인할 수 있었다. 이러한 규모의 전유전체 결합 생존 분석은 최초로 시도된 것이다.Chapter1Prologue 1 1.1 Introduction 1 1.2 Accessible High-Performance Computing Systems 4 1.2.1 Preliminaries 4 1.2.2 Multiple CPU nodes: clusters, supercomputers, and clouds 7 1.2.3 Multi-GPU node 9 1.3 Highly Parallelizable Algorithms 12 1.3.1 MM algorithms 12 1.3.2 Proximal gradient descent 14 1.3.3 Proximal distance algorithm 16 1.3.4 Primal-dual methods 17 Chapter 2 Easily Parallelizable and Distributable Class of Algorithms for Structured Sparsity, with Optimal Acceleration 20 2.1 Introduction 20 2.2 Unification of Algorithms LV and CV (g ≡ 0) 30 2.2.1 Relation between Algorithms LV and CV 30 2.2.2 Unified algorithm class 34 2.2.3 Convergence analysis 35 2.3 Optimal acceleration 39 2.3.1 Algorithms 40 2.3.2 Convergence analysis 41 2.4 Stochastic optimal acceleration 45 2.4.1 Algorithm 45 2.4.2 Convergence analysis 47 2.5 Numerical experiments 50 2.5.1 Model problems 50 2.5.2 Convergence behavior 52 2.5.3 Scalability 62 2.6 Discussion 63 Chapter 3 Towards Unified Programming for High-Performance Statistical Computing Environments 66 3.1 Introduction 66 3.2 Related Software 69 3.2.1 Message-passing interface and distributed array interfaces 69 3.2.2 Unified array interfaces for CPU and GPU 69 3.3 Easy-to-use Software Libraries for HPC 70 3.3.1 Deep learning libraries and HPC 70 3.3.2 Case study: PyTorch versus TensorFlow 73 3.3.3 A brief introduction to PyTorch 76 3.3.4 A brief introduction to Julia 80 3.3.5 Methods and multiple dispatch 80 3.3.6 Multidimensional arrays 82 3.3.7 Matrix multiplication 83 3.3.8 Dot syntax for vectorization 86 3.4 Distributed matrix data structure 87 3.4.1 Distributed matrices in PyTorch: distmat 87 3.4.2 Distributed arrays in Julia: MPIArray 90 3.5 Examples 98 3.5.1 Nonnegative matrix factorization 100 3.5.2 Positron emission tomography 109 3.5.3 Multidimensional scaling 113 3.5.4 L1-regularized Cox regression 117 3.5.5 Genome-wide survival analysis of the UK Biobank dataset 121 3.6 Discussion 126 Chapter 4 Conclusion 131 Appendix A Monotone Operator Theory 134 Appendix B Proofs for Chapter II 139 B.1 Preconditioned forward-backward splitting 139 B.2 Optimal acceleration 147 B.3 Optimal stochastic acceleration 158 Appendix C AWS EC2 and ParallelCluster 168 C.1 Overview 168 C.2 Glossary 169 C.3 Prerequisites 172 C.4 Installation 173 C.5 Configuration 173 C.6 Creating, accessing, and destroying the cluster 178 C.7 Installation of libraries 178 C.8 Running a job 179 C.9 Miscellaneous 180 Appendix D Code for memory-efficient L1-regularized Cox proportional hazards model 182 Appendix E Details of SNPs selected in L1-regularized Cox regression 184 Bibliography 188 국문초록 212Docto