STK4080 SURVIVAL AND EVENT HISTORY ANALYSIS Slides 1 ... · Bo Lindqvist Slides 1: Introduction...
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STK4080 SURVIVAL AND EVENT HISTORYANALYSIS
Slides 1: Introduction
Bo LindqvistDepartment of Mathematical Sciences
Norwegian University of Science and TechnologyTrondheim
https://www.ntnu.no/ansatte/[email protected]
University of Oslo, Autumn 2019
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THIS COURSE ...
Course content
The course gives an introduction to the most important concepts andmethods in survival and event history analysis. These methods haveapplications for instance in insurance, medicine and reliability.
Learning outcome
The course gives the background for analyzing a wide specter of modelsfor time to one event as well as models for complex event histories. Thecourse gives a practical introduction to these methods as well as atheoretical understanding of them.
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EXAMPLES OF LIFETIMES (WIDELY DEFINED)
Lifetime
Time to occurrence of some event of interest for individuals in somepopulation.
Medical research:
Time to death of a patient after start of certain treatment
Time from entrance to discharge from a hospital
Times between successive epileptic seizures for patient
Reliability engineering:
Time to failure of a component or a system
Number of cycles to failure (fatigue testing)
Times between successive failures of a machine
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WHY COLLECT AND ANALYZE
LIFETIME/SURVIVAL/RELIABILITY DATA?
Medical research:
Compare different treatments with respect to survival or recurrence
Predict the outcome of an intervention or the life expectancy afterthe invention
Identify risk factors for diseases and assess their magnitude
Reliability engineering:
Assess reliability of a system/component/product
Compare two or more products with respect to reliability
Predict product reliability in the design phase
Predict warranty claims for a product in the market
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SPECIAL ASPECTS OF LIFETIME ANALYSIS
IN STATISTICS
Censored data (how can we use data from individuals or units forwhich the event of interest has not occurred within the observationperiod?)
subjects may not be followed from time 0 (in the study time scale),but only from a later entry time. This is called delayed entry orleft-truncation.
Definition of starting time and failure time may be difficult
Definition of time scale (in reliability: operation time, calendar timeor number of cycles?)
Effect of covariates (demographic, medical, environmental)
What if an individual or unit dies or fails of another cause than theone we would like to study? (”competing risks”)
Recurrent events – what if the system can fail several times; how toanalyze recurring stages of a disease?
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COMPLETE DATA: BALL BEARING FAILURE DATA
Data: Millions of revolutions to fatigue failure for 23 units
Question: How can we fit a parametric lifetime distribution to these data?
17,88 28,92 33,00 41,52 42,12 45,60 48,40 51,8451,96 54,12 55,56 67,80 68,64 68,64 68,88 84,1293,12 98,64 105,12 105,84 127,92 128,04 173,40
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BALL BEARING FAILURE DATA (EVENT PLOT)
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HEAVILY CENSORED DATA: IC DATA (MEEKER, 1987)
Questions of interest:
How to estimate the distribution of the failure time when there arecensored observations?
Probability of failure before 100 hours?
Failure rate by 100 hours?
Proportion failed after 105 hours?
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IC DATA (EVENT PLOT)
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SURVIVAL OF MULTIPLE MYELOMA PATIENTS
Multiple myeloma is a malignant disease characterised by theaccumulation of abnormal plasma cells, a type of white blood cell, inthe bone marrow.
Data (next slide) from Medical Center of the University of WestVirginia, USA.
Aim: To examine the association between certain explanatoryvariables or covariates and the survival time of patients in monthsfrom diagnosis until death from multiple myeloma).
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MULTIPLE MYELOMA DATA
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TYPICAL EXAM EXERCISE CASE
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MULTISTATE MODELS: COMPETING RISKS
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MULTISTATE MODELS: ILLNESS-DEATH MODEL
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RECURRENT EVENTS
� -� -� -�T1 T2 T3
-r r rt0 S1 S2 S3
For each individual in the study we observe repeated occurrences of anevent, for example
1 Relapse from disease (epileptic seizures, recurrence of tumors)
2 System is repaired and put into use again.
3 Machine part is replaced.
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EXAMPLE OF DATA FOR RECURRENT EVENTS
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AALEN AND HUSEBY: Movements of the small bowel
See Example 7.1 in ABG:
The task is to estimate the average duration of the intervals and thevariation within and between individuals
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RECURRENT EVENTS/REPAIRABLE SYSTEMS
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VALVE SEAT REPLACEMENT DATA
Data on previous slide are collected from valve seats from a fleet of 41diesel engines. Each engine has 16 valves. (Time unit is days of operation).
Questions of interest:
Does the replacement rate increase with age?
How many replacement valves will be needed in the future?
Can valve life in these systems be modeled as a renewal process?
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ESTIMATED NUMBER OF VALVE SEAT REPLACEMENTS
Middle curve is cumulative estimated number of replacements for oneengine, as a function of age.
Lower and upper curves are 95% confidence limits.
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