【书名】 Dynamic Regression Models for Survival Data
【作者】 Torben Martinussen (Author), Thomas H. Scheike
【出版社】Springer
【版本】第一版
【出版日期】March 20, 2006
【文件格式】PDF
【文件大小】9.17MB
【页数】470 pages
【ISBN出版号】978-0387202747
【资料类别】统计学,生存统计
【市面定价】N/A
【扫描版还是影印版】 清晰版
【是否缺页】无
【关键词】 Survival analysis,time dependent covariates, Dynamic Regression, biostatistics
【内容简介】介绍生存分析的一本好书。理论和应用并重。

 

【目录】
Preface vii
1 Introduction 1
1.1 Survivaldata .......................... 1
1.2 Longitudinaldata........................ 14
2 Probabilistic background 17
2.1 Preliminaries .......................... 17
2.2 Martingales ........................... 20
2.3 Countingprocesses....................... 23
2.4 Markedpointprocesses .................... 30
2.5 Large-sampleresults ...................... 34
2.6 Exercises ............................ 44
3 Estimation for filtered counting process data 49
3.1 Filteredcountingprocessdata................. 49
3.2 Likelihoodconstructions.................... 62
3.3 Estimatingequations...................... 70
3.4 Exercises ............................ 74
4 Nonparametric procedures for survival data 81
4.1 TheKaplan-Meierestimator.................. 81
4.2 Hypothesistesting ....................... 86
4.2.1 Comparisons of groups of survival data . . . . . . . 86
4.2.2 Stratifiedtests ..................... 93
4.3 Exercises ............................ 95
5 Additive Hazards Models 103
5.1 Additivehazardsmodels.................... 108
5.2 Inferenceforadditivehazardsmodels............. 116
5.3 Semiparametricadditivehazardsmodels........... 126
5.4 Inference for the semiparametric hazards model . . . . . . . 135
5.5 Estimatingthesurvivalfunction ............... 146
5.6 Additiveratemodels...................... 149
5.7 Goodness-of-fitprocedures................... 151
5.8 Example............................. 159
5.9 Exercises ............................ 165
6 Multiplicative hazards models 175
6.1 TheCoxmodel ......................... 181
6.2 Goodness-of-fit procedures for the Cox model . . . . . . . . 193
6.3 Extended Cox model with time-varying regression effects . . 205
6.4 InferencefortheextendedCoxmodel ............ 213
6.5 A semiparametric multiplicative hazards model . . . . . . . 218
6.6 Inference for the semiparametric multiplicative model . . . 224
6.7 Estimatingthesurvivalfunction ............... 226
6.8 Multiplicativeratemodels................... 227
6.9 Goodness-of-fitprocedures................... 228
6.10Examples ............................ 234
6.11Exercises ............................ 240
7 Multiplicative-Additive hazards models 249
7.1 TheCox-Aalenhazardsmodel................. 251
7.1.1 Modelandestimation ................. 252
7.1.2 Inference and large sample properties . . . . . . . . 255
7.1.3 Goodness-of-fitprocedures............... 260
7.1.4 Estimating the survival function . . . . . . . . . . . 266
7.1.5 Example......................... 270
7.2 Proportionalexcesshazardsmodel .............. 273
7.2.1 Modelandscoreequations............... 274
7.2.2 Estimationandinference ............... 276
7.2.3 Efficientestimation................... 280
7.2.4 Goodness-of-fitprocedures............... 283
7.2.5 Examples ........................ 284
7.3 Exercises ............................ 290
8 Accelerated failure time and transformation models 293
8.1 The accelerated failure time model . . . . . . . . . . . . . . 294
8.2 Thesemiparametrictransformationmodel.......... 298
8.3 Exercises ............................ 309
9 Clustered failure time data 313
9.1 Marginal regression models for clustered failure time data . 314
9.1.1 Working independence assumption . . . . . . . . . . 315
9.1.2 Two-stage estimation of correlation . . . . . . . . . . 327
9.1.3 One-stage estimation of correlation . . . . . . . . . . 330
9.2 Frailtymodels.......................... 334
9.3 Exercises ............................ 338
10 Competing Risks Model 347
10.1Productlimitestimator .................... 351
10.2Causespecifichazardsmodeling................ 356
10.3Subdistributionapproach ................... 361
10.4Exercises ............................ 370
11 Marked point process models 375
11.1 Nonparametric additive model for longitudinal data . . . . 380
11.2 Semiparametric additive model for longitudinal data . . . . 389
11.3Efficientestimation....................... 393
11.4Marginalmodels ........................ 397
11.5Exercises ............................ 408
A Khmaladze’s transformation 411
B Matrix derivatives 415
C The Timereg survival package for R 417
Bibliography 453
Index 467

【整理书评】

"This book is a welcome addition to the literature on survival analysis for several reasons. The coverage of both multiplicative and, especially, additive models with time-varying covariates is well beyond that found in other books. There is also more emphasis on model checking than in most books. … the book is enjoyable to read. … This book is an important resource for anyone with an interest in survival or event history analysis." (J. F. Lawless, Short Book Reviews, Vol. 26 (2), 2006)

"‘Dynamic regression models’ … are able to capture time-varying dynamics of covariate effects. … this book provides a timely summary of the results for topics which are important to practical applications. The readers who are interested in further research in these areas will find the detailed derivations of mathematical results helpful. … The rich exercises at the end of each chapter make this book an excellent choice as a textbook for an advanced survival analysis course." (Dongsheng Tu, Zentrablatt MATH, Vol. 1096 (22), 2006)

"Survival data analysis has been a very active research field for several decades. An important contribution that stimulated the entire field was the counting process formulation … . that is also used in this monograph. … There are exercises at the end of each chapter … . The practical aspects of survival analysis are illustrated with a set of worked out examples using R. … The book is primarily aimed at the biostatistical community … . It is well written … ." (Rainer Schlittgen, Statistical Papers, Vol. 48 (3), 2007)

"The book under review is a welcome addition to existing excellent books on survival analysis … . It should be a useful reference to both applied as well as theoretical bio-statisticians. Perhaps it could also be used as a text for a graduate level course in survival analysis." (Subhash C. Kochar, Mathematical Reviews, Issue 2007 b)

"This book is aimed at advanced graduate students and statistical researchers in statistics/biostatistics departments. … The inspiration and influence of Andersen et al. (1993) on the presentation style, terminology, and approach to the subject are very visible in many parts of the book. … In summary, this book definitely deserves a place in the collection of any serious survival analyst. It is also recommended to theoretically sound data analysts interested in dynamic and semiparametric survival models beyond the class of multiplicative models." (Debajyoti Sinha, Journal of the American Statistical Association, Vol. 102 (480), 2007)