LEADER 05436nam 2200697Ia 450 001 9911019797303321 005 20200520144314.0 010 $a9786610839506 010 $a9781280839504 010 $a1280839503 010 $a9780470510445 010 $a0470510447 010 $a9780470510438 010 $a0470510439 035 $a(CKB)1000000000356142 035 $a(EBL)291012 035 $a(SSID)ssj0000203525 035 $a(PQKBManifestationID)11183238 035 $a(PQKBTitleCode)TC0000203525 035 $a(PQKBWorkID)10173711 035 $a(PQKB)11400145 035 $a(MiAaPQ)EBC291012 035 $a(OCoLC)144865393 035 $a(Perlego)2771492 035 $a(EXLCZ)991000000000356142 100 $a20061025d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMissing data in clinical studies /$fGeert Molenberghs, Michael G. Kenward 210 $aChichester, Eng. ;$aHoboken, NJ $cJ. Wiley & Sons$dc2007 215 $a1 online resource (528 p.) 225 1 $aStatistics in practice 300 $aDescription based upon print version of record. 311 08$a9780470849811 311 08$a0470849819 320 $aIncludes bibliographical references (p. 483-496) and index. 327 $aMissing Data in Clinical Studies; Contents; Preface; Acknowledgements; I Preliminaries; 1 Introduction; 1.1 From Imbalance to the Field of Missing Data Research; 1.2 Incomplete Data in Clinical Studies; 1.3 MAR, MNAR, and Sensitivity Analysis; 1.4 Outline of the Book; 2 Key Examples; 2.1 Introduction; 2.2 The Vorozole Study; 2.3 The Orthodontic Growth Data; 2.4 Mastitis in Dairy Cattle; 2.5 The Depression Trials; 2.6 The Fluvoxamine Trial; 2.7 The Toenail Data; 2.8 Age-Related Macular Degeneration Trial; 2.9 The Analgesic Trial; 2.10 The Slovenian Public Opinion Survey 327 $a3 Terminology and Framework3.1 Modelling Incompleteness; 3.2 Terminology; 3.3 Missing Data Frameworks; 3.4 Missing Data Mechanisms; 3.5 Ignorability; 3.6 Pattern-Mixture Models; Part II Classical Techniques and the Need for Modelling; 4 A Perspective on Simple Methods; 4.1 Introduction; 4.1.1 Measurement model; 4.1.2 Method for handling missingness; 4.2 Simple Methods; 4.2.1 Complete case analysis; 4.2.2 Imputation methods; 4.2.3 Last observation carried forward; 4.3 Problems with Complete Case Analysis and Last Observation Carried Forward 327 $a4.4 Using the Available Cases: a Frequentist versus a Likelihood Perspective4.4.1 A bivariate normal population; 4.4.2 An incomplete contingency table; 4.5 Intention to Treat; 4.6 Concluding Remarks; 5 Analysis of the Orthodontic Growth Data; 5.1 Introduction and Models; 5.2 The Original, Complete Data; 5.3 Direct Likelihood; 5.4 Comparison of Analyses; 5.5 Example SAS Code for Multivariate Linear Models; 5.6 Comparative Power under Different Covariance Structures; 5.7 Concluding Remarks; 6 Analysis of the Depression Trials; 6.1 View 1: Longitudinal Analysis 327 $a6.2 Views 2a and 2b and All versus Two Treatment ArmsIII Missing at Random and Ignorability; 7 The Direct Likelihood Method; 7.1 Introduction; 7.2 Ignorable Analyses in Practice; 7.3 The Linear Mixed Model; 7.4 Analysis of the Toenail Data; 7.5 The Generalized Linear Mixed Model; 7.6 The Depression Trials; 7.7 The Analgesic Trial; 8 The Expectation-Maximization Algorithm; 8.1 Introduction; 8.2 The Algorithm; 8.2.1 The initial step; 8.2.2 The E step; 8.2.3 The M step; 8.3 Missing Information; 8.4 Rate of Convergence; 8.5 EM Acceleration; 8.6 Calculation of Precision Estimates 327 $a8.7 A Simple Illustration8.8 Concluding Remarks; 9 Multiple Imputation; 9.1 Introduction; 9.2 The Basic Procedure; 9.3 Theoretical Justification; 9.4 Inference under Multiple Imputation; 9.5 Efficiency; 9.6 Making Proper Imputations; 9.7 Some Roles for Multiple Imputation; 9.8 Concluding Remarks; 10 Weighted Estimating Equations; 10.1 Introduction; 10.2 Inverse Probability Weighting; 10.3 Generalized Estimating Equations for Marginal Models; 10.3.1 Marginal models for non-normal data; 10.3.2 Generalized estimating equations; 10.3.3 A method based on linearization 327 $a10.4 Weighted Generalized Estimating Equations 330 $aMissing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described. Provides a practical guide to the analysis of clinical trials and related studies with missing data.Examines 410 0$aStatistics in practice. 606 $aClinical trials$xStatistical methods 606 $aMissing observations (Statistics) 615 0$aClinical trials$xStatistical methods. 615 0$aMissing observations (Statistics) 676 $a610.724 700 $aMolenberghs$b Geert$0259367 701 $aKenward$b Michael G.$f1956-$0102987 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019797303321 996 $aMissing data in clinical studies$94418151 997 $aUNINA LEADER 05471nam 2200685Ia 450 001 9911019669003321 005 20200520144314.0 010 $a9786613294593 010 $a9781283294591 010 $a1283294591 010 $a9781118032701 010 $a1118032705 010 $a9781118030950 010 $a1118030958 035 $a(CKB)2670000000122271 035 $a(EBL)694688 035 $a(OCoLC)761319792 035 $a(SSID)ssj0000554934 035 $a(PQKBManifestationID)11364361 035 $a(PQKBTitleCode)TC0000554934 035 $a(PQKBWorkID)10518430 035 $a(PQKB)10559350 035 $a(MiAaPQ)EBC694688 035 $a(Perlego)2761945 035 $a(EXLCZ)992670000000122271 100 $a19970408d1997 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aInterior point algorithms $etheory and analysis /$fYinyu Ye 210 $aNew York $cWiley$dc1997 215 $a1 online resource (438 p.) 225 1 $aWiley-Interscience series in discrete mathematics and optimization 300 $aDescription based upon print version of record. 311 08$a9780471174202 311 08$a0471174203 320 $aIncludes bibliographical references (p. 365-408) and index. 327 $aInterior Point Algorithms: Theory and Analysis; Contents; Preface; List of Figures; 1 Introduction and Preliminaries; 1.1 Introduction; 1.2 Mathematical Preliminaries; 1.2.1 Basic notations; 1.2.2 Convex sets; 1.2.3 Real functions; 1.2.4 Inequalities; 1.3 Decision and Optimization Problems; 1.3.1 System of linear equations; 1.3.2 System of nonlinear equations; 1.3.3 Linear least-squares problem; 1.3.4 System of linear inequalities; 1.3.5 Linear programming (LP); 1.3.6 Quadratic programming (QP); 1.3.7 Linear complementarity problem (LCP); 1.3.8 Positive semi-definite programming (PSP) 327 $a1.3.9 Nonlinear programming (NP)1.3.10 Nonlinear complementarity problem (NCP); 1.4 Algorithms and Computation Models; 1.4.1 Worst-case complexity; 1.4.2 Condition-based complexity; 1.4.3 Average complexity; 1.4.4 Asymptotic complexity; 1.5 Basic Computational Procedures; 1.5.1 Gaussian elimination method; 1.5.2 Choleski decomposition method; 1.5.3 The Newton method; 1.5.4 Solving ball-constrained linear problem; 1.5.5 Solving ball-constrained quadratic problem; 1.6 Notes; 1.7 Exercises; 2 Geometry of Convex Inequalities; 2.1 Convex Bodies; 2.1.1 Center of gravity; 2.1.2 Ellipsoids 327 $a2.2 Analytic Center2.2.1 Analytic center; 2.2.2 Dual potential function; 2.2.3 Analytic central-section inequalities; 2.3 Primal and Primal-Dual Potential Functions; 2.3.1 Primal potential function; 2.3.2 Primal-dual potential function; 2.4 Potential Functions for LP, LCP, and PSP; 2.4.1 Primal potential function for LP; 2.4.2 Dual potential function for LP; 2.4.3 Primal-dual potential function for LP; 2.4.4 Potential function for LCP; 2.4.5 Potential function for PSP; 2.5 Central Paths of LP, LCP, and PSP; 2.5.1 Central path for LP; 2.5.2 Central path for LCP; 2.5.3 Central path for PSP 327 $a2.6 Notes2.7 Exercises; 3 Computation of Analytic Center; 3.1 Proximity to Analytic Center; 3.2 Dual Algorithms; 3.2.1 Dual Newton procedure; 3.2.2 Dual potential algorithm; 3.2.3 Central-section algorithm; 3.3 Primal Algorithms; 3.3.1 Primal Newton procedure; 3.3.2 Primal potential algorithm; 3.3.3 Affine scaling algorithm; 3.4 Primal-Dual (Symmetric) Algorithms; 3.4.1 Primal-dual Newton procedure; 3.4.2 Primal-dual potential algorithm; 3.5 Notes; 3.6 Exercises; 4 Linear Programming Algorithms; 4.1 Karmarkar's Algorithm; 4.2 Path-Following Algorithm; 4.3 Potential Reduction Algorithm 327 $a4.4 Primal-Dual (Symmetric) Algorithm4.5 Adaptive Path-Following Algorithms; 4.5.1 Predictor-corrector algorithm; 4.5.2 Wide-neighborhood algorithm; 4.6 Affine Scaling Algorithm; 4.7 Extensions to QP and LCP; 4.8 Notes; 4.9 Exercises; 5 Worst-Case Analysis; 5.1 Arithmetic Operation; 5.2 Termination; 5.2.1 Strict complementarity partition; 5.2.2 Project an interior point onto the optimal face; 5.3 Initialization; 5.3.1 A HSD linear program; 5.3.2 Solving (HSD); 5.3.3 Further analysis; 5.4 Infeasible-Starting Algorithm; 5.5 Notes; 5.6 Exercises; 6 Average-Case Analysis; 6.1 One-Step Analysis 327 $a6.1.1 High-probability behavior 330 $aThe first comprehensive review of the theory and practice of one of today's most powerful optimization techniques.The explosive growth of research into and development of interior point algorithms over the past two decades has significantly improved the complexity of linear programming and yielded some of today's most sophisticated computing techniques. This book offers a comprehensive and thorough treatment of the theory, analysis, and implementation of this powerful computational tool.Interior Point Algorithms provides detailed coverage of all basic and advanced aspects of th 410 0$aWiley-Interscience series in discrete mathematics and optimization. 606 $aProgramming (Mathematics) 606 $aLinear programming 615 0$aProgramming (Mathematics) 615 0$aLinear programming. 676 $a519.7/2 700 $aYe$b Yinyu$0771525 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019669003321 996 $aInterior point algorithms$94416944 997 $aUNINA