Survival Analysis with Correlated Endpoints

Joint Frailty-Copula Models

AvTakeshi Emura,Shigeyuki Matsui

Häftad, Engelska, 2019

Del i serien SpringerBriefs in Statistics

640 kr

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Beskrivning

This book introduces readers to advanced statistical methods for analyzing survival data involving correlated endpoints. Hence, the book offers an essential reference guide for medical statisticians and provides researchers with advanced, innovative statistical tools.

Produktinformation

Utgivningsdatum:2019-04-04
Mått:155 x 235 x 8 mm
Vikt:219 g
Format:Häftad
Språk:Engelska
Serie:SpringerBriefs in Statistics
Antal sidor:118
Förlag:Springer Verlag, Singapore
ISBN:9789811335150

Utforska kategorier

Mer om författaren

Takeshi Emura, Chang Gung UniversityShigeyuki Matsui, Department of Biostatistics, Nagoya University Graduate School of Medicine Virginie Rondeau, INSERM U 1219

Recensioner i media

“This book can be used as a textbook for a course aimed at postgraduate students in biostatistics and medicine.” (Denis Sidorov, zbMATH 1429.62003, 2020)

Innehållsförteckning

Chapter 1: Setting the scene.-1.1 Endpoints.- 1.2 Benefits of investigating correlated endpoints.- 1.3 Copulas and frailty: a brief history.- References.- Chapter 2: Introduction to survival analysis .-2.1 Endpoint and censoring.- 2.2 Kaplan-Meier estimator and survival function.- 2.3 Hazard function.- 2.4 Log-rank test for two-sample comparison.- 2.5 Cox regression.- 2.6 Example of Cox regression.- 2.7 Likelihood inference under non-informative censoring.- 2.8 Theoretical notes.- 2.9 Exercises.- References.- Chapter 3: The joint frailty-copula model for correlated endpoints.- 3.1 Introduction.- 3.2 Semi-competing risks data.- 3.3 Joint frailty-copula model.- 3.4 Penalized likelihood with splines.- 3.5 Case study: ovarian cancer data.- 3.6 Technical note 1: Numerical maximization of the penalized likelihood.- 3.7 Technical note 2: LCV and choice of and .- 3.8 Exercises.- References.- Chapter 4: High-dimensional covariates in the joint frailty-copula model.- 4.1 Introduction.- 4.2 Tukey’s compound covariate.- 4.3 Univariate selection.- 4.4 Meta-analytic data with high-dimensional covariates.- 4.5 The joint model with compound covariates .- 4.6 The joint model with ridge or Lasso predictor .- 4.7 Prediction of patient-level survival function .- 4.8 Simulations.- 4.8.1 Simulation design.- 4.8.2 Simulation results.- 4.9 Case study: ovarian cancer data .- 4.9.1 Compound covariate.- 4.9.2 Fitting the joint frailty-copula mode.- 4.9.3 Patient-level survival function.- 4.10 Concluding remarks.- References.- Chapter 5: Dynamic prediction of time-to-death.- 5.1 Accurate prediction of survival.- 5.2 Framework of dynamic prediction.- 5.2.1 Conditional failure function given tumour progression.- 5.2.2 Conditional hazard function given tumour progression.- 5.3 Prediction formulas under the joint frailty-copula model.- 5.4 Estimating prediction formulas.- 5.5 Case study: ovarian cancer data.- 5.6 Discussions.- References.- Chapter 6: Future developments- 6.1 Analysis of recurrent events.- 6.2 Kendall’s tau in meta-analysis.- 6.3 Validation of surrogate endpoints.- 6.4 Left-truncation.- 6.5 Interactions.- 6.6 Parametric failure time models.- 6.7 Compound covariate.- References.- Appendix A: Cubic spline bases.- Appendix B: R codes for the ovarian cancer data analysis.- B1. Using CXCL12 gene as a covariate.- B2. Using compound covariates (CCs) and residual tumour as covariates.- Appendix C: Derivation of prediction formulas.