Download Data Mining in a Pharmaceutical Environment

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Data Mining in a
Pharmaceutical
Environment
Franky De Cooman
SPS (Europe)
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Overview of presentation
• Difference between Data Mining and
‘usual’ statistics
• Examples of Data Mining in the
pharmaceutical environment
• Case Study in a Health Economics
Department
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Statistics
• Business question
• Formulate
Hypothesis
• Design data
collection
=> model is defined
• Collect data
• Perform modeling &
and check validity of
model
Data Mining
• Repository of Data
• Need to exploit data
• Look in the data
(dredge)
• Formulate Hypothesis
• Perform models
• Compare models
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Examples
Research
Health-economics
Development
Manufacturing
Outcomes Research
Marketing
Pharmaco-vigilance
Finance
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Case Study
Comparing the cost of
Pain-Relief Drugs
Sponsored by Johnson & Johnson Health Economics
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Data Mining project
Data
expertise
Business
expertise
Analytical
expertise
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Multidisciplinary Team
• Guy Nuyts, J&J Health-Economics, Executive Director
• Franky De Cooman, SPS, Business Area Coordinator
Pharms, Senior Consultant
• Members of the Data Mining & Statistics team
• Jean-Michel Bodart, SPS, Medical Doctor
• Annette De Reytere, SPS, Medical/statistical/technical
writer
• Christine Vander Vorst, SPS, Mgr. SAS consultancy
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‘Business’ Problem
Data
expertise
Business
expertise
Analytical
expertise
! Is TTS-Fentanyl a cost-effective painmanagement drug compared to
other strong opioids?
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‘Data’ Problem
Data
expertise
Business
expertise
Analytical
expertise
Data from Private Insurance company,
containing all costs for 1450 patients taking
strong opioids in 1997-1998, mostly cancer
patients
Costs & reasons for
Doctor visits
Hospital
Drug acquisition
Nurse visits
Emergency Room
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Problem Translation
– Look for possible differences between the TTSFentanyl and the morphine population,
– Look out for the probability of switching treatments,
– Investigate upon the driving factors explaining the costs
of patients.
Data
expertise
Business
expertise
Analytical
expertise
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Problem Translation
– Answer these questions using
• Logistic Regression
• Log-Linear Modelling&
Data
expertise
Business
expertise
• Clustering Analysis
• ANOVA
– Split data in two ‘equal’ parts
• Train: to construct the model
• Validate: to validate the model
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Analytical
expertise
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Treatment coding
•Patients who start with Morphine and do not switch to
another treatment are identified as MM.
•Patients who start with TTS-Fentanyl and do not switch to
Morphine are taken under DD.
•Some patients start with Morphine and switch to
TTS-Fentanyl, they are the MD cases.
•Others start with TTS-Fentanyl and switch over to
Morphine, the DM patients.
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Probability of switching
• Treatment groups are not comparable as to cancer type; e.g.
more bone and connective tissues cancers in the DD group
• People switching to TTS-Fentanyl continue taking
Morphine, but is mostly bolus morphine. No confirmation on
dependence on cancer type.
• Probability of switching varies from 6 to 22% depending on
cancer type, and also depends on the amount of bolus taken.
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Concomitant drugs
• Looking at the drugs bought, an idea is obtained of the AE’s
• MM and DD group cannot be compared
• Look in the MD group at ‘the act of buying drugs’ before
and after switching, each patient serving as his own control
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Concomitant drugs
26 drugs are bought ‘less’
LAXATIVE
MORF(Intake in Morphine period)
FENT(Intake in Durogesic period)
Frequency‚
Percent ‚
Row Pct ‚
Col Pct ‚
0‚
1‚ Total
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
0 ‚
6 ‚
8 ‚
14
‚
6.90 ‚
9.20 ‚ 16.09
‚ 42.86 ‚ 57.14 ‚
‚ 15.79 ‚ 16.33 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
1 ‚
32 ‚
41 ‚
73
‚ 36.78 ‚ 47.13 ‚ 83.91
‚ 43.84 ‚ 56.16 ‚
‚ 84.21 ‚ 83.67 ‚
ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ
Total
38
49
87
43.68
56.32
100.00
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Cost analyses in DM
• Opioid costs goes from 15.4 to 86.8, ie + 71.4
• Other costs (26 drugs) goes from 146.9 to 28 ie - 118.9
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Complete cost analyses
All costs (doctor, nurse, drug, hospital, pain, …) were coded
_
0
1
2
No information on the cost
has nothing to do with the cancer treatment
can be attributed to the cancer treatment
can probably be attributed to the cancer treatment
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Complete cost analyses
Pain treatment cost varies between 3 and
6 % of total cost
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Variable
drcosd_
drcosd0
drcose1
drcosd2
ambcosd_
ambcosd0
ambcosd1
ambcosd2
hspcosd_
hspcosd0
hspcosd1
hspcosd2
prccosd_
prccosd1
prccosd1
Prccosd2
ercosd
paincosd
Drug
0.5640
0.4246
0.2907
0.8395
0.4312
0.9972
0.0297
0.2736
0.9038
0.9674
0.8444
0.0130
0.9840
0.0001
0.1082
0.1606
0.6049
0.0001
Cancer
0.5290
0.4784
0.0001
0.0041
0.4872
0.9292
0.0001
0.0762
0.7042
0.8594
0.0008
0.0110
0.2861
0.0001
0.0001
0.0002
0.2619
0.0367
Sex
0.1558
0.9982
0.6292
0.1170
0.5401
0.2935
0.0635
0.2285
0.1217
0.4141
0.8858
0.0623
0.5210
0.0581
0.0484
0.1420
0.1762
0.0431
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Age
0.8068
0.1017
0.8317
0.8663
0.0001
0.3221
0.0001
0.0001
0.4735
0.0880
0.0013
0.0147
0.2994
0.0001
0.0128
0.0001
0.6092
0.0396
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Clustering costs
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Clustering costs
The probability of belonging to a cluster depends on
• the cancer
• the gender of the patient
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Conclusions
• The acquisition costs of drugs seems to go
down after switching in DM group
• The choice of pain relief treatment is not a
driving cost factor
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Offices Based In:
United Kingdom Holland
Belgium
South Africa
Australia
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