LEADER 04364nam 2200637Ia 450 001 9910809954703321 005 20200520144314.0 010 $a1-281-17991-4 010 $a9786611179915 010 $a0-387-68639-8 024 7 $a10.1007/978-0-387-68639-4 035 $a(CKB)1000000000399546 035 $a(EBL)337200 035 $a(OCoLC)437205698 035 $a(SSID)ssj0000229362 035 $a(PQKBManifestationID)11239447 035 $a(PQKBTitleCode)TC0000229362 035 $a(PQKBWorkID)10168179 035 $a(PQKB)11271819 035 $a(DE-He213)978-0-387-68639-4 035 $a(MiAaPQ)EBC337200 035 $a(Au-PeEL)EBL337200 035 $a(CaPaEBR)ebr10223254 035 $a(CaONFJC)MIL117991 035 $a(PPN)123740436 035 $a(EXLCZ)991000000000399546 100 $a20080103d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aProportional hazards regression /$fJohn O'Quigley 205 $a1st ed. 2008. 210 $aNew York $cSpringer$dc2008 215 $a1 online resource (556 p.) 225 1 $aStatistics for biology and health 300 $aDescription based upon print version of record. 311 $a0-387-25148-0 320 $aIncludes bibliographical references and index. 327 $aBackground: Probability -- Background: General inference -- Background: Survival analysis -- Marginal survival -- Regression models and subject heterogeneity -- Inference: Estimating equations -- Inference: Functions of Brownian motion -- Inference: Likelihood -- Inference: Stochastic integrals -- Inference: Small samples -- Inference: Changepoint models -- Explained variation -- Explained randomness -- Survival given covariates -- Proofs of theorems, lemmas and corollaries. 330 $aThe place in survival analysis now occupied by proportional hazards models and their generalizations is so large that it is no longer conceivable to offer a course on the subject without devoting at least half of the content to this topic alone. This book focuses on the theory and applications of a very broad class of models?proportional hazards and non-proportional hazards models, the former being viewed as a special case of the latter?which underlie modern survival analysis. Unlike other books in this area the emphasis is not on measure theoretic arguments for stochastic integrals and martingales. Instead, while inference based on counting processes and the theory of martingales is covered, much greater weight is placed on more traditional results such as the functional central limit theorem. This change in emphasis allows us in the book to devote much greater consideration to practical issues in modeling. The implications of different models, their practical interpretation, the predictive ability of any model, model construction, and model selection as well as the whole area of mis-specified models receive a great deal of attention. The book is aimed at both those interested in theory and those interested in applications. Many examples and illustrations are provided. The required mathematical and statistical background for those relatively new to the field is carefully outlined so that the material is accessible to a broad range of levels. John O?Quigley?Director of Research at the French Institut National de la Santé et de la Recherche Médicale and Professor of Mathematics at the University of California at San Diego?has published extensively on the subject of survival analysis, both in theoretical and applied journals. He has taught and carried out collaborative research at several of the world's leading departments of mathematics and statistics including the University of Washington, the Fred Hutchinson Cancer Research Center in Seattle, Harvard University, and Lancaster University, UK. 410 0$aStatistics for biology and health. 606 $aRegression analysis 606 $aHazardous substances$xRisk assessment 615 0$aRegression analysis. 615 0$aHazardous substances$xRisk assessment. 676 $a519.536 700 $aO'Quigley$b John$0847320 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910809954703321 996 $aProportional hazards regression$93916893 997 $aUNINA