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

01 Introduction

Agenda

02 Survival analysis: origin and possible application

03 IBM SPSS modeler and Survival Analysis

04 Closing remarks

05 Q&A

4C Consulting | Our Customers

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Marketing Excellence

Customer Experience Management

Customer Insight Management

Experience Identity | Customer Journeys | Moments of Truth | Cross-channel | Unique Customer View | CRM Roadmap | Cultural Change

About 4C Consulting | Our service portfolio

Sales Excellence Service Excellence

• Marketing Maturity Assessment

• Campaign Management & Automation

• Campaign Management Outsourcing

• Marketing Resource Management

• SFA Management & Automation

• Sales Portfolio Management

• Sales Middle Office

• Training & Coaching

• Customer Service Automation

• Self Service Strategy & Management

• Complaints Handling

Performance Management | Data Quality | Data Mining I Segmentation | Scoring | Profiling | Forecasting | Predictive Analytics

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BI / CI Services

Customers

How to optimize your

service to answer their

needs

Who are they / What do they want

How to communicate

with them

DATA

01 Introduction

Agenda

02 Survival analysis: origin and possible application

03 IBM SPSS modeler and Survival Analysis

04 Closing remarks

05 Q&A

1 Introductory example

Attention:

• Preferably use 4CC library images (see 4CPedia or ask Priskilla)

• If not: make sure that image resolution is sufficiently high! No blurry pictures

• Text bar should be moved for optimal

position in picture

• Text color is white or black in function of

contrast needs

• Font = calibri, 44pt, regular

• Alignment: default = left

The origin

How to treat?

Metastatic cancer

Classic treatment

30% alive

New treatment

50% alive AFTER 12 MONTHS

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Metastatic cancer

Classic treatment

20% alive

New treatment

21% alive

How to treat?

AFTER 16 MONTHS

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Attention:

• Preferably use 4CC library images (see 4CPedia or ask Priskilla)

• If not: make sure that image resolution is sufficiently high! No blurry pictures

• Text bar should be moved for optimal

position in picture

• Text color is white or black in function of

contrast needs

• Font = calibri, 44pt, regular

• Alignment: default = left

Time is Crucial

Interesting, but how can we use this?

Attention:

• Preferably use 4CC library images (see 4CPedia or ask Priskilla)

• If not: make sure that image resolution is sufficiently high! No blurry pictures

• Text bar should be moved for optimal

position in picture

• Text color is white or black in function of

contrast needs

• Font = calibri, 44pt, regular

• Alignment: default = left

An Application

Attention:

• Preferably use 4CC library images (see 4CPedia or ask Priskilla)

• If not: make sure that image resolution is sufficiently high! No blurry pictures

• Text bar should be moved for optimal

position in picture

• Text color is white or black in function of

contrast needs

• Font = calibri, 44pt, regular

• Alignment: default = left

Customer Churn

How to treat?

Churn

Classic marketing program

30% stays

New marketing program

50% stays AFTER 12 MONTHS

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Churn

Classic marketing program

20% stays

New marketing program

21% stays

How to treat?

AFTER 16 MONTHS

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Attention:

• Preferably use 4CC library images (see 4CPedia or ask Priskilla)

• If not: make sure that image resolution is sufficiently high! No blurry pictures

• Text bar should be moved for optimal

position in picture

• Text color is white or black in function of

contrast needs

• Font = calibri, 44pt, regular

• Alignment: default = left

Time is Money

2 The idea

Survival curves | Customer Example

New marketing program

Classic marketing program

Event=churn

Time (months)

Survival probability

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Survival curves | Traditional Example

New treatment

Classic treatment

Event=death

Time (months)

Survival probability

Median survival time: 9.6 versus 8 months

Douillard JY et al. J Clin Oncol 2010; 28 (31): 4697-4705

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Added value | Entire Sample

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Start of study End of study

Time (months)

=event occurs

=enter the study

2 4 6 8 10 12 0

Added value | Entire Sample

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Start of study End of study

Time (months)

=event occurs

=enter the study

2 4 6 8 10 12 0

Added value | Entire Sample

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Start of study End of study

Time (months)

=event occurs

=enter the study

2 4 6 8 10 12 0

Added value | Entire Sample

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Time (months)

=event occurs

2 4 6 8 10 12 0

Time in study =censored

An individual censored at time t should have the same survival

chance as all subject who survive up to time t

Condition | Non-informative censoring

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Hospital A Hospital B

Condition | Non-informative censoring

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Hospital A Hospital B

Time (months)

2 4 6 8 10 12 0 Time (months)

2 4 6 8 10 12 0

Condition | Non-informative censoring

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Hospital A Hospital B

Condition | Non-informative censoring

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Hospital A Hospital B

Time (months)

2 4 6 8 10 12 0 Time (months)

2 4 6 8 10 12 0

Condition | Non-informative censoring

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Compare 2 loyalty programs

For who: valuable customers (gold status)

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Condition | Non-informative censoring

Condition | Non-informative censoring

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Bank A Bank B

Condition | Non-informative censoring

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How is your data collected?

Which customers are included?

Comparing survival curves

Treatment A

Treatment B

Survival probability

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Comparing survival curves

New marketing program Classic marketing program

Event=churn

Time (months)

Survival probability

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Randomised trial All patients Treatment A

Treatment B

Follow-up

Follow-up

Compare results

RANDOM

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Observational study All patients Treatment A

Treatment B

Follow-up

Follow-up

Compare results

CHOICE

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Observational study All patients Campaign A

Campaign B

Follow-up

Follow-up

Compare results

CHOICE

Business setting

We need to adjust for

confounders

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3 Modelling

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0%

20%

40%

60%

80%

100%

0 1 2 3 4 5 6 7 8 9 10 11 12

Definitions

S(t)=Survival curve F(t)=Cumulative Incidence

Time (months)

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Definitions

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0%

5%

10%

15%

20%

25%

30%

0 1 2 3 4 5 6 7 8 9 10 11

Incidence Hazard

Definitions

Time Survival Curve Cumulative incidence Incidence Hazard

0 100% 0% 20% 20%

1 80% 20% 20% 25%

2 60% 40% 10% 17%

3 50% 50%

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Definitions

Time Survival Curve Cumulative incidence Incidence Hazard

0 100% 0% 20% 20%

1 80% 20% 20% 25%

2 60% 40% 10% 17%

3 50% 50%

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Definitions

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0%

5%

10%

15%

20%

25%

30%

0 1 2 3 4 5 6 7 8 9 10 11

Incidence Hazard

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

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(*)Goetghebeur E and Van Rompaye B. Survival analysis edition 2011

Cox proportional hazard model

𝜆 𝑡, 𝒁 = 𝜆0 𝑡 𝑒𝑥𝑝 𝛽1𝑍1 + 𝛽2𝑍2+…+𝛽𝑝𝑍𝑝

𝜆0=baseline hazard

𝑍1, 𝑍2,… , 𝑍𝑝= covariates

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Cox proportional hazard model

𝜆 𝑡, 𝒁 = 𝜆0 𝑡 𝑒𝑥𝑝(𝛽1𝑍1)

𝜆0=baseline hazard

𝑍1 = 0 = 𝑛𝑜 𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡

1 = 𝑡𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡

𝛽1=-0.7 exp(𝛽1)=0.5

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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

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01 Introduction

Agenda

02 Survival analysis: origin and possible application

03 IBM SPSS modeler and Survival Analysis

04 Closing remarks

05 Q&A

Questions?

Let’s have coffee first

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