LEADER 03892nam 22006975 450 001 9910254075403321 005 20200705155913.0 010 $a3-662-49332-2 024 7 $a10.1007/978-3-662-49332-8 035 $a(CKB)3710000000645573 035 $a(EBL)4504399 035 $a(SSID)ssj0001665933 035 $a(PQKBManifestationID)16454433 035 $a(PQKBTitleCode)TC0001665933 035 $a(PQKBWorkID)15000849 035 $a(PQKB)11297983 035 $a(DE-He213)978-3-662-49332-8 035 $a(MiAaPQ)EBC4504399 035 $a(PPN)193441942 035 $a(EXLCZ)993710000000645573 100 $a20160411d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe Cox Model and Its Applications /$fby Mikhail Nikulin, Hong-Dar Isaac Wu 205 $a1st ed. 2016. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2016. 215 $a1 online resource (131 p.) 225 1 $aSpringerBriefs in Statistics,$x2191-544X 300 $aDescription based upon print version of record. 311 $a3-662-49331-4 320 $aIncludes bibliographical references and index. 327 $aIntroduction: Several Classical Data Examples for Survival Analysis -- Elements of Survival Analysis -- The Cox Proportional Hazards Model -- The AFT, GPH, LT, Frailty, and GLPH Models -- Cross-effect Models of Survival Functions -- The Simple Cross-effect Model -- Goodness-of-Fit for the Cox Model -- Remarks on Computations in Parametric and Semiparametric Estimation -- Cox Model for Degradation and Failure Time Data -- References -- Index. 330 $aThis book will be of interest to readers active in the fields of survival analysis, genetics, ecology, biology, demography, reliability and quality control. Since Sir David Cox?s pioneering work in 1972, the proportional hazards model has become the most important model in survival analysis. The success of the Cox model stimulated further studies in semiparametric and nonparametric theories, counting process models, study designs in epidemiology, and the development of many other regression models that could offer more flexible or more suitable approaches in data analysis. Flexible semiparametric regression models are increasingly being used to relate lifetime distributions to time-dependent explanatory variables. Throughout the book, various recent statistical models are developed in close connection with specific data from experimental studies in clinical trials or from observational studies. 410 0$aSpringerBriefs in Statistics,$x2191-544X 606 $aStatistics  606 $aBiostatistics 606 $aEpidemiology 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aBiostatistics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15020 606 $aEpidemiology$3https://scigraph.springernature.com/ontologies/product-market-codes/H63000 615 0$aStatistics . 615 0$aBiostatistics. 615 0$aEpidemiology. 615 14$aStatistical Theory and Methods. 615 24$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aBiostatistics. 615 24$aEpidemiology. 676 $a615.580724 700 $aNikulin$b Mikhail$4aut$4http://id.loc.gov/vocabulary/relators/aut$0756104 702 $aWu$b Hong-Dar Isaac$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254075403321 996 $aThe Cox Model and Its Applications$92044171 997 $aUNINA