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.