## Survival and longitudinal data Analysis

Survival time data: some notable features . . . . . . . . . . . . .

Censoring and truncation of survival time data . . . . . .

Continuous versus discrete (or grouped) survival time data

Why are distinctive statistical methods used? . . . . . . . . . . .

Problems for OLS caused by right censoring . . . . . . . .

Time-varying covariates and OLS . . . . . . . . . . . . . .

Structural.modelling and OLS . . . . . . . . . . . . . . .

Why not use binary dependent variable models rather

• than OLS? . . . . . . . . . . . .
• 2. Introduction to survival analysis:
• Definitions and examples of survival data
• Properties of survival Data,
• Sampling methods,
• Censoring (types of censoring),
•  Likelihood function
• 3.Estimation of Survival functions

Survivor function

Hazard function

Cummulative hazard function

Relationship between the functions

Kaplan-Meier survival curves

, Graphical Display for Survival functions

4. Parametric Lifetime Distributions

Exponential

Weibull

Lognormal

Gamma

Gompertz

The Cox-Proportional Hazards regression model with one and several covariates

Adequacy assessment of the Proportional Hazards (PH) model, Time dependent extension of the Cox model,

Advanced topics in Survival analysis: Correlated survival time models (such as shared frailty model

• Parametric,Nonparametric and semiparametric models in survival analysis
• 6.Introduction to longitudinal data: data structure and notation, exploring longitudinal data, conceptual framework for continuous response, models for correlation structure; exploring correlation structure, discrete response
• 7.Handling and describing longitudinal data: maximum likelihood estimation under normality, restricted maximum likelihood, large sample inference, implications of missing data.
• 8. Longitudinal study designs, models for two measures, (random effects) marginal models, dynamic (autoregressive) models, latent class models, and models for multivariate outcomes, fixed, random and mixed effects linear models; diagnostics and model checking; and missing data and nonresponse issues.
• 9. Multilevel analysis of longitudinal data, handling attrition of subjects in longitudinal studies during multilevel analysis
• Reporting and interpreting results from longitudinal data analysis.