LEADER 02912oam 2200625I 450 001 9910778962903321 005 20230802004316.0 010 $a0-429-09433-7 010 $a1-280-12190-4 010 $a9786613525765 010 $a1-4398-3543-8 024 7 $a10.1201/b11311 035 $a(CKB)2550000000079048 035 $a(EBL)830223 035 $a(OCoLC)773034860 035 $a(SSID)ssj0000580660 035 $a(PQKBManifestationID)11385063 035 $a(PQKBTitleCode)TC0000580660 035 $a(PQKBWorkID)10606643 035 $a(PQKB)10310743 035 $a(MiAaPQ)EBC830223 035 $a(Au-PeEL)EBL830223 035 $a(CaPaEBR)ebr10522550 035 $a(CaONFJC)MIL352576 035 $a(EXLCZ)992550000000079048 100 $a20180331d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDynamic prediction in clinical survival analysis /$fHans van Houwelingen, Hein Putter 210 1$aBoca Raton :$cCRC Press,$d2012. 215 $a1 online resource (250 p.) 225 1 $aMonographs on statistics and applied probability ;$v123 300 $a"A Chapman & Hall book." 311 $a1-4398-3533-0 320 $aIncludes bibliographical references. 327 $aThe special nature of survival data -- Cox regression model -- Measuring the predictive value of a Cox model -- Calibration and revision of Cox models -- Mechanisms explaining violation of the Cox model -- Non-proportional hazards models -- Dealing with non-proportional hazards -- Dynamic predictions using biomarkers -- Dynamic prediction in multi-state models -- Dynamic prediction in chronic disease -- Penalized Cox models -- Dynamic prediction based on genomic data. 330 $aIn the last twenty years, dynamic prediction models have been extensively used to monitor patient prognosis in survival analysis. Written by one of the pioneers in the area, this book synthesizes these developments in a unified framework. It covers a range of models, including prognostic and dynamic prediction of survival using genomic data and time-dependent information. The text includes numerous examples using real data that is taken from the authors collaborative research. R programs are provided for implementing the methods--Provided by publisher. 410 0$aMonographs on statistics and applied probability ;$v123. 606 $aProportional hazards models 606 $aSurvival analysis (Biometry) 615 0$aProportional hazards models. 615 0$aSurvival analysis (Biometry) 676 $a615.580724 700 $aHouwelingen$b J. C. van.$01489124 701 $aPutter$b Hein$01489125 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910778962903321 996 $aDynamic prediction in clinical survival analysis$93709673 997 $aUNINA