Obiettivi

Expcted Learning Outcomes
Types of Survival data - classical, competing risks, multistate, illness-death
Features of Survival data - Censoring, delayed entry, time varying covariates, treatment switch
Time functions for Classical Survival data – Survival, Incidence, Hazard, Dynamic Incidence
Time functions for Competing Risks data – Crude Incidences, Cause-specific Hazards
Time functions for Multistate data – State Probabilities, Transition hazards
Assumption for the Transition hazard – Markov, Semi-Markov, Extended Semi-Markov
Counterfactual Time functions for Competing risks data – Net Incidence, Cause-specific Hazards
Counterfactual Time functions for Illness(tr switch)-death data – Survival “as if” under the initial treatment, Survival “as if” treatment is switched at baseline
Non parametric estimation for Competing risks data – Aalen-Johansen estimator of the Crude Incidences, Nelson-Aalen estimator for the Cause-specific hazards
Non parametric estimation for Illness-death data – Aalen Johansen estimator of the State Probabilities, Nelson-Aalen estimator for the Cause-specific hazards
Validation of Assumptions for the Transition hazard – Graphical with non parametric smoothing, by Cox model, by Poisson model
Non parametric estimation of Counterfactual Time functions for Illness(trt switch)-death data – Kaplan Meier estimator
Semi-parametric modelling of the Transition hazards – Cox model, Poisson model, role of time scales

Contenuti sintetici

Competing Risks and Multistate models are widely used in medical research when survival data involve composite outcomes with absorbing events (competing risks) and possible intermediate events (multistate). These models can be used to obtain risk prediction on future outcome development and to assess the prognostic impact of patient characteristics and therapeutic interventions on the outcome development

Prediction. The incidence function is generalized to competing risks data by its decomposition into absolute risks of each absorbing event. Absolute risks are called “crude probabilities” emphasizing the indirect protection that each absorbing event determines on the others. In the presence of multistate data, the intermediate events are included in the absolute risks which are called “state probabilities”, emphasizing that subjects may sojourn in intermediate states in the multistate process that takes to the absorbing events

Prognostic Impact. The hazard function is generalized to competing risks data by its decomposition into cause specific hazard of each absorbing event. In the presence of multistate data, the intermediate events are included among the hazard functions that are called “transition hazard” between the states, to emphasize the motion among the intermediate and absorbing states

The course will deal first with the clinical questions and corresponding theoretical quantities. This link will be crucial to guide the student in the approach to estimation and inference to be adopted

Programma esteso

SURVIVAL DATA
Brief reminder

ABSOLUTE RISKS AND STATE PROBABILITIES
Motivating examples
Clinical Questions
Non parametric analysis
Semi-Parametric models
Practical session with R

TRANSITION HAZARDS
Motivating examples
Clinical Questions
Markov, Semimarkov, Extended Semi Markov concepts
Non parametric analysis
Semi-Parametric models

PRACTICAL SESSIONS
Theoretical reasoning and appications using the R software


Prerequisiti

Basic knowledge of survival analysis
Participants are required to bring their own laptops with the most recent copy of R and RStudio installed
The following R packages will be used in the tutorial: mstate, cmprsk, survival, epi

Modalità didattica

Interactive lectures and practicals

Materiale didattico

Handouts and R code

Periodo di erogazione dell'insegnamento

October 15th
9.00-10:30  Lecture and Practical
10.50-12:30 Lecture and Practical
14:00-15.30 Lecture and Practical
15:50-17.00 Lecture and Practical

October 16th
9.00-10:30  Lecture and Practical
10.50-12:30 Lecture and Practical
14:00-15.30 Lecture and Practical
15:50-17.00  Lecture and Practical

Modalità di verifica del profitto e valutazione

On line quiz

Orario di ricevimento

Aims

Expcted Learning Outcomes
Types of Survival data - classical, competing risks, multistate, illness-death
Features of Survival data - Censoring, delayed entry, time varying covariates, treatment switch
Time functions for Classical Survival data – Survival, Incidence, Hazard, Dynamic Incidence
Time functions for Competing Risks data – Crude Incidences, Cause-specific Hazards
Time functions for Multistate data – State Probabilities, Transition hazards
Assumption for the Transition hazard – Markov, Semi-Markov, Extended Semi-Markov
Counterfactual Time functions for Competing risks data – Net Incidence, Cause-specific Hazards
Counterfactual Time functions for Illness(tr switch)-death data – Survival “as if” under the initial treatment, Survival “as if” treatment is switched at baseline
Non parametric estimation for Competing risks data – Aalen-Johansen estimator of the Crude Incidences, Nelson-Aalen estimator for the Cause-specific hazards
Non parametric estimation for Illness-death data – Aalen Johansen estimator of the State Probabilities, Nelson-Aalen estimator for the Cause-specific hazards
Validation of Assumptions for the Transition hazard – Graphical with non parametric smoothing, by Cox model, by Poisson model
Non parametric estimation of Counterfactual Time functions for Illness(trt switch)-death data – Kaplan Meier estimator
Semi-parametric modelling of the Transition hazards – Cox model, Poisson model, role of time scales

Contents

Competing Risks and Multistate models are widely used in medical research when survival data involve composite outcomes with absorbing events (competing risks) and possible intermediate events (multistate). These models can be used to obtain risk prediction on future outcome development and to assess the prognostic impact of patient characteristics and therapeutic interventions on the outcome development

Prediction. The incidence function is generalized to competing risks data by its decomposition into absolute risks of each absorbing event. Absolute risks are called “crude probabilities” emphasizing the indirect protection that each absorbing event determines on the others. In the presence of multistate data, the intermediate events are included in the absolute risks which are called “state probabilities”, emphasizing that subjects may sojourn in intermediate states in the multistate process that takes to the absorbing events

Prognostic Impact. The hazard function is generalized to competing risks data by its decomposition into cause specific hazard of each absorbing event. In the presence of multistate data, the intermediate events are included among the hazard functions that are called “transition hazard” between the states, to emphasize the motion among the intermediate and absorbing states

The course will deal first with the clinical questions and corresponding theoretical quantities. This link will be crucial to guide the student in the approach to estimation and inference to be adopted

Detailed program

SURVIVAL DATA
Brief reminder

ABSOLUTE RISKS AND STATE PROBABILITIES
Motivating examples
Clinical Questions
Non parametric analysis
Semi-Parametric models
Practical session with R

TRANSITION HAZARDS
Motivating examples
Clinical Questions
Markov, Semimarkov, Extended Semi Markov concepts
Non parametric analysis
Semi-Parametric models

PRACTICAL SESSIONS
Theoretical reasoning and appications using the R software


Prerequisites

Basic knowledge of survival analysis
Participants are required to bring their own laptops with the most recent copy of R and RStudio installed
The following R packages will be used in the tutorial: mstate, cmprsk, survival, epi

Teaching form

Interactive lectures and practicals

Textbook and teaching resource

Handouts and R code

Semester

October 15th
9.00-10:30  Lecture and Practical
10.50-12:30 Lecture and Practical
14:00-15.30 Lecture and Practical
15:50-17.00 Lecture and Practical

October 16th
9.00-10:30  Lecture and Practical
10.50-12:30 Lecture and Practical
14:00-15.30 Lecture and Practical
15:50-17.00  Lecture and Practical

Assessment method

On line quiz

Office hours

Metodi di iscrizione

  • Iscrizione manuale
  • Accesso ospiti
  • Iscrizione spontanea (Studente)

Staff

    Docente

  • Immagine Laura Antolini
    Laura Antolini