LEADER 05511nam 2200733 450 001 9910810323603321 005 20200520144314.0 010 $a1-118-77824-3 010 $a1-322-52060-7 010 $a1-118-77823-5 010 $a1-118-77821-9 035 $a(CKB)3710000000315846 035 $a(EBL)1896285 035 $a(SSID)ssj0001380857 035 $a(PQKBManifestationID)11769365 035 $a(PQKBTitleCode)TC0001380857 035 $a(PQKBWorkID)11372242 035 $a(PQKB)10887378 035 $a(MiAaPQ)EBC1896285 035 $a(DLC) 2014027681 035 $a(MiAaPQ)EBC4038612 035 $a(Au-PeEL)EBL1896285 035 $a(CaPaEBR)ebr11000349 035 $a(CaONFJC)MIL683342 035 $a(OCoLC)897448783 035 $a(Au-PeEL)EBL4038612 035 $a(CaPaEBR)ebr11112481 035 $a(PPN)191922692 035 $a(EXLCZ)993710000000315846 100 $a20150114h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aApplied mixed models in medicine /$fHelen Brown, Robin Prescott 205 $aThird edition. 210 1$aChichester, England :$cWiley,$d2015. 210 4$d©2015 215 $a1 online resource (539 p.) 225 1 $aStatistics in Practice 300 $aDescription based upon print version of record. 311 $a1-118-77825-1 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Preface to third edition; Mixed models notation; About the Companion Website; Chapter 1 Introduction; 1.1 The use of mixed models; 1.2 Introductory example; 1.2.1 Simple model to assess the effects of treatment (Model A); 1.2.2 A model taking patient effects into account (Model B); 1.2.3 Random effects model (Model C); 1.2.4 Estimation (or prediction) of random effects; 1.3 A multi-centre hypertension trial; 1.3.1 Modelling the data; 1.3.2 Including a baseline covariate (Model B); 1.3.3 Modelling centre effects (Model C) 327 $a1.3.4 Including centre-by-treatment interaction effects (Model D)1.3.5 Modelling centre and centre·treatment effects as random (Model E); 1.4 Repeated measures data; 1.4.1 Covariance pattern models; 1.4.2 Random coefficients models; 1.5 More about mixed models; 1.5.1 What is a mixed model?; 1.5.2 Why use mixed models?; 1.5.3 Communicating results; 1.5.4 Mixed models in medicine; 1.5.5 Mixed models in perspective; 1.6 Some useful definitions; 1.6.1 Containment; 1.6.2 Balance; 1.6.3 Error strata; Chapter 2 Normal mixed models; 2.1 Model definition; 2.1.1 The fixed effects model 327 $a2.1.2 The mixed model2.1.3 The random effects model covariance structure; 2.1.4 The random coefficients model covariance structure; 2.1.5 The covariance pattern model covariance structure; 2.2 Model fitting methods; 2.2.1 The likelihood function and approaches to its maximisation; 2.2.2 Estimation of fixed effects; 2.2.3 Estimation (or prediction) of random effects and coefficients; 2.2.4 Estimation of variance parameters; 2.3 The Bayesian approach; 2.3.1 Introduction; 2.3.2 Determining the posterior density; 2.3.3 Parameter estimation, probability intervals and p-values 327 $a2.3.4 Specifying non-informative prior distributions2.3.5 Evaluating the posterior distribution; 2.4 Practical application and interpretation; 2.4.1 Negative variance components; 2.4.2 Accuracy of variance parameters; 2.4.3 Bias in fixed and random effects standard errors; 2.4.4 Significance testing; 2.4.5 Confidence intervals; 2.4.6 Checking model assumptions; 2.4.7 Missing data; 2.4.8 Determining whether the simulated posterior distribution has converged; 2.5 Example; 2.5.1 Analysis models; 2.5.2 Results; 2.5.3 Discussion of points from Section 2.4; Chapter 3 Generalised linear mixed models 327 $a3.1 Generalised linear models3.1.1 Introduction; 3.1.2 Distributions; 3.1.3 The general form for exponential distributions; 3.1.4 The GLM definition; 3.1.5 Fitting the GLM; 3.1.6 Expressing individual distributions in the general exponential form; 3.1.7 Conditional logistic regression; 3.2 Generalised linear mixed models; 3.2.1 The GLMM definition; 3.2.2 The likelihood and quasi-likelihood functions; 3.2.3 Fitting the GLMM; 3.3 Practical application and interpretation; 3.3.1 Specifying binary data; 3.3.2 Uniform effects categories; 3.3.3 Negative variance components 327 $a3.3.4 Presentation of fixed and random effects estimates 330 $aA fully updated edition of this key text on mixed models, focusing on applications in medical research The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott's groundbreaking text 410 0$aStatistics in practice. 606 $aMedicine 615 0$aMedicine. 676 $a610.72/7 700 $aBrown$b Helen$f1962-$01672557 702 $aPrescott$b Robin 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910810323603321 996 $aApplied mixed models in medicine$94035982 997 $aUNINA