1. Breakfast seminar: The Business Value of Survival Analysis
Evi Nagler
Methodologist - European Renal Best Practice
Renal Unit, Ghent University Hospital
Veerle Liébaut
Consultant – 4C Consulting
Wannes Rosius,
Client Technical Professional - IBM SPSS

2. Agenda
01
Introduction
02
Survival analysis: origin and possible application
03
IBM SPSS modeler and Survival Analysis
04
Closing remarks
05
Q&A

3. 4C Consulting | Our Customers
3

4. About 4C Consulting | Our service portfolio
Customer Experience Management
Experience Identity | Customer Journeys | Moments of Truth | Cross-channel | Unique Customer View | CRM Roadmap | Cultural Change
Marketing Excellence
•
•
•
•
Marketing Maturity Assessment
Campaign Management & Automation
Campaign Management Outsourcing
Marketing Resource Management
Sales Excellence
•
•
•
•
SFA Management & Automation
Sales Portfolio Management
Sales Middle Office
Training & Coaching
Service Excellence
• Customer Service Automation
• Self Service Strategy & Management
• Complaints Handling
Customer Insight Management
Performance Management | Data Quality | Data Mining I Segmentation | Scoring | Profiling | Forecasting | Predictive Analytics
4

5. BI / CI Services
How to
communicate
with them
Customers
How to
optimize your
service to
answer their
needs
Who are they
/ What do
they want
DATA
5

6. Agenda
01
Introduction
02
Survival analysis: origin and possible application
03
IBM SPSS modeler and Survival Analysis
04
Closing remarks
05
Q&A

7. 1
Introductory example

8. The origin

9. How to treat?
Metastatic
cancer
Classic
treatment
AFTER 12 MONTHS
New
treatment
30% alive
50% alive
9

10. How to treat?
Metastatic
cancer
Classic
treatment
AFTER 16 MONTHS
New
treatment
20% alive
21% alive
10

11. Time is Crucial

12. Interesting, but how can we use this?

13. An Application

14. Customer Churn

15. How to treat?
Churn
Classic
marketing
program
AFTER 12 MONTHS
New
marketing
program
30% stays
50% stays
15

16. How to treat?
Churn
Classic
marketing
program
AFTER 16 MONTHS
New
marketing
program
20% stays
21% stays
16

17. Time is Money

18. 2
The idea

19. Survival curves | Customer Example
Survival
probability
Event=churn
New marketing program
Classic marketing program
Time (months)
19

20. Survival curves | Traditional Example
Survival
probability
Event=death
New treatment
Classic treatment
Median survival time: 9.6 versus 8 months
Time (months)
Douillard JY et al. J Clin Oncol 2010; 28 (31): 4697-4705
20

21. Added value | Entire Sample
=event occurs
=enter the study
Start of study
0
2
4
6
8
10
End of study
12
Time (months)
21

22. Added value | Entire Sample
=event occurs
=enter the study
Start of study
0
2
4
6
8
10
End of study
12
Time (months)
22

23. Added value | Entire Sample
=event occurs
=enter the study
Start of study
0
2
4
6
8
10
End of study
12
Time (months)
23

24. Added value | Entire Sample
=event occurs
=censored
Time in study
An individual censored at time t
should have the same survival
chance as all subject who survive up
to time t
0
2
4
6
8
10
12
Time (months)
24

25. Condition | Non-informative censoring
Hospital A
Hospital B
25

26. Condition | Non-informative censoring
Hospital A
0
2
4
6
Time (months)
8
Hospital B
10
12
0
2
4
6
8
10
12
Time (months)
26

27. Condition | Non-informative censoring
Hospital A
Hospital B
27

28. Condition | Non-informative censoring
Hospital A
0
2
4
6
Time (months)
8
Hospital B
10
12
0
2
4
6
8
10
12
Time (months)
28

29. Condition | Non-informative censoring
29

30. Condition | Non-informative censoring
Compare 2 loyalty programs
For who: valuable customers (gold status)
30

31. Condition | Non-informative censoring
Bank A
Bank B
31

32. Condition | Non-informative censoring
32

33. How is your data collected?
Which customers are included?
33

34. Comparing survival curves
Survival
probability
Treatment A
Treatment B
34

35. Comparing survival curves
Survival
probability
Event=churn
New marketing program
Classic marketing program
Time (months)
35

36. Randomised trial
Treatment A
All patients
Follow-up
Compare results
RANDOM
Treatment B
Follow-up
36

37. Observational study
Treatment A
All patients
Follow-up
Compare results
CHOICE
Treatment B
Follow-up
37

38. Observational study
Campaign A
All patients
Follow-up
Business setting
Compare results
CHOICE
Campaign B
Follow-up
We need to
adjust for
confounders
38

39. 3
Modelling
39

40. Definitions
100%
F(t)=Cumulative Incidence
S(t)=Survival curve
80%
60%
40%
20%
0%
0
1
2
3
4
5
6
7
8
9
10
11
12
Time (months)
40

41. Definitions
30%
Incidence
Hazard
25%
20%
15%
10%
5%
0%
0
1
2
3
4
5
6
7
8
9
10
11
41

42. Definitions
Time
Cumulative
Survival Curve incidence
Incidence
Hazard
0
100%
0%
20%
20%
1
80%
20%
20%
25%
2
60%
40%
10%
17%
3
50%
50%
42

43. Definitions
Time
Cumulative
Survival Curve incidence
Incidence
Hazard
0
100%
0%
20%
20%
1
80%
20%
20%
25%
2
60%
40%
10%
17%
3
50%
50%
43

44. Definitions
30%
Incidence
Hazard
25%
20%
15%
10%
5%
0%
0
1
2
3
4
5
6
7
8
9
10
11
44

45. Cox proportional hazards model
Most common used model for survival data (*)
Flexible choice of covariates
Fairly easy to model
Standard software exists
Well developed elegant mathematical theory
Few distributional assumptions
Non informative censoring
Proportional hazards
Independence
(*)Goetghebeur E and Van Rompaye B. Survival analysis edition 2011
45

46. Cox proportional hazard model
𝜆 𝑡, 𝒁 = 𝜆0 𝑡 𝑒𝑥𝑝 𝛽1 𝑍1 + 𝛽2 𝑍2 +…+𝛽 𝑝 𝑍 𝑝
𝜆0 =baseline hazard
𝑍1 , 𝑍2 ,… , 𝑍 𝑝 = covariates
46

47. Cox proportional hazard model
𝜆 𝑡, 𝒁 = 𝜆0 𝑡 𝑒𝑥𝑝(𝛽1 𝑍1 )
𝜆0 =baseline hazard
0 = 𝑛𝑜 𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡
𝑍1 =
1 = 𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡
𝛽1 =-0.7  exp(𝛽1 )=0.5
47

48. Take home messages
Classic regression ignores time – time is crucial
Solution: survival analysis
Advantages
 Use of entire sample
 Instantaneous risk estimation
Conditions
 Non informative censoring
 Proportional hazards
 Independence
48

49. Agenda
01
Introduction
02
Survival analysis: origin and possible application
03
IBM SPSS modeler and Survival Analysis
04
Closing remarks
05
Q&A

50. Questions?
Let’s have coffee first
50