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\n1.1 History ......................................... 3  \n1.2 Survival data ...................................... 4  \n1.3 Overview ........................................ 6  \n1.4 Mathematical Notation ................................ 7  \n2 Survival curves 9  \n2.1 One event type, one event per subject ........................ 9  \n2.2 Repeated events .................................... 14  \n2.3 Competing risks .................................... 17  \n2.3.1 Simple example ................................. 17  \n2.3.2 Monoclonal gammopathy ........................... 20  \n2.4 Multi-state data .................................... 23  \n2.4.1 Myeloid data .................................. 24  \n2.5 Influence matrix .................................... 37  \n2.6 Differences in survival ................................. 39  \n2.7 Robust variance ..................................... 39  \n2.8 State space figures ................................... 40  \n2.8.1 Further notes .................................. 41  \n3 Cox model 43  \n3.1 One event type, one event per subject ........................ 43  \n3.2 Repeating Events .................................... 49  \n3.3 Competing risks .................................... 52  \n3.3.1 MGUS ...................................... 52  \n3.4 Multiple event types and multiple events per subject ................ 57  \n3.4.1 Data ....................................... 58  \n3.4.2 Fits ....................................... 59  \n3.4.3 Timeline data .................................. 66  \n3.5 Testing proportional hazards ............................. 69  \n3.5.1 Constructed variables ............................. 69  \n3.5.2 Score tests ................................... 71  \n3.5.3 Computational details ............................. 73  \n3.6 Profile likelihood .................................... 74  \n4 Accelerated Failure Time models 77  \n4.1 Usage .......................................... 77  \n4.2 Strata .......................................... 78  \n4.3 Penalized models .................................... 78  \n4.4 Specifying a distribution ................................ 79  \n4.5 Residuals ........................................ 81  \n4.5.1 Response .................................... 81  \n4.5.2 Deviance .................................... 81  \n4.5.3 Dfbeta ...................................... 81  \n4.5.4 Working ..................................... 82  \n4.5.5 Likelihood displacement residuals ....................... 82  \n4.6 Predicted values .................................... 82  \n4.6.1 Linear predictor and response ......................... 82  \n4.6.2 Terms ...................................... 82  \n4.6.3 Quantiles .................................... 83  \n4.7 Fitting the model .................................... 84  \n4.8 Derivatives ....................................... 86  \n4.9 Distributions ...................................... 87  \n4.9.1 Gaussian .................................... 87  \n4.9.2 Extreme value ................................. 87  \n4.9.3 Logistic ..................................... 88  \n4.9.4 Other distributions ............................... 88  \n5 Tied event times 90  \n5.1 Cox model estimates .................................. 90  \n5.2 Cumulative hazard and survival ............................ 93  \n5.3 Predicted cumulative hazard and survival from a Cox model ............ 93  \n6 Multi-state models 95  \nA Changes from version 2.44 to 3.1 96  \nA.1 Changes in version 3 .................................. 96  \nA.2 Survfit .......................................... 97  \nA.3 Coxph .......................................... 98  \nChapter 1  \nIntroduction  \n1.1 History  \nWork on the survival package began in 1985 in connection with the analysis of medical research data, without any realization at the time that the work would become a package. Eventually, the software","cbCaimPOpE1sKlbg","https://ap.wps.com/l/cbCaimPOpE1sKlbg","pdf",710173,100,"English","# Contents\n## 1 Introduction\n## 2 Survival curves\n## 3 Cox model\n## 4 Accelerated Failure Time models\n## 5 Tied event times\n## 6 Multi-state models","[{\"question\":\"What is the survival package in R primarily designed to do?\",\"answer\":\"It is designed to solve real survival analysis questions that arise from real datasets, with functions written to address practical analytical needs.\"},{\"question\":\"Why did the author believe the package became successful?\",\"answer\":\"A primary reason was that the functions were developed to address genuine analysis problems from real data, while theoretical issues were explored only when necessary.\"},{\"question\":\"What are the main goals of version 3.x of the survival package?\",\"answer\":\"Version 3 focuses on making multi-state curves and models easier to use, providing deeper support for absolute risk estimates, using robust variance estimates consistently, and cleaning up naming inconsistencies.\"}]","survival.pdf - Survival Package in R - Introduction | PDF",1790329474,252]