Real World Evidence stream - SAS Proceedings and …PhUSE 2016 RW04 Oct 2016 Asthma/COPD Algorithm 1...
Transcript of Real World Evidence stream - SAS Proceedings and …PhUSE 2016 RW04 Oct 2016 Asthma/COPD Algorithm 1...
1 Asthma/COPD Algorithm PhUSE 2016 RW04 Oct 2016
Real World Evidence stream
An Algorithm to distinguish between COPD and asthma
Berber T. Snoeijer, Jetty A. Overbeek
PHARMO Institute for Drug Outcomes Research,
Utrecht, Netherlands
Berber Snoeijer
October 2016
PhUSE 2016 RW04
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Marketing of drugs
• What drug is used
• Where is it used
• To whom is it given
• At what price
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Marketing of drugs
• What drug is used
• Where is it used
• To whom is it given
• At what price
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No indication
• Not in de database
• Not allowed to link
• Not available
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Linkage of databases
GP database
Outpatient pharmacy database
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Overlap
• GP diagnosis: ICPC R.. (respiratory)
• Pharmacy: ATC R03…. (respiratory)
• > 20,000 patients
Outpatient Pharmacy
data
GP data
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To build a model
• Linkage of databases
• Exploratory analysis
• Logistic regression
– 2 models
• Finetuning
• Estimating the cut-off
• Validation
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Exploratory analysis
• Examine Differences between indication groups
– Asthma
– COPD
– Allergic conditions
– Other
• Statistics
– Descriptive
– T-tests / Chi-square
– Correlations / Regression
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Exploratory analysis
• Age
• Gender
• Drugs
• Drug classes
• Drug combinations
• Prescriber (GP / specialist)
• Duration of use
• Number of prescriptions
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Exploratory analysis
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Prepare dataset
• 1 row per patient
• Explanatory variables
– Drug x? –> 1/0
– Age class
– Duration of use
– …
• Outcome variables
– COPD? -> 1/0
– Asthma? -> 1/0
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Logistic regression
proc logistic data=lngWrk descending
OUTest=TmpEst outmodel=data.model1;
class agecat gender ;
model COPD= AgeCat gender Med1 Med2 Med3 … /
selection=backward slstay=0.001;
run;
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Finetuning
• C-statistic (concordance)
• Significance vs influence
• Correlation between explanatory variables
• Comparison of models for consecutive years
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The COPD model
Age
Gender
Duration of use
R03
tiotropium
R01
R03BA ipratropium
LABA (no FDC)
montelukast
ICS
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Estimating the cut-off
PROC LOGISTIC inmodel=data.model1;
score data=InputData OUT=plong
(RENAME=(P_1=pcopd) DROP=p_0);
RUN;
• Sensitivity, specificity, NPV, PPV
• GP ratios
• Expected ratios based on literature
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Expected ratios based on literature
R03 users
asthma ≈ 50%
COPD ≈ 30 %
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Sensitivity
• Sensitivity 77.3%
COPD GP records
Yes No
Model prediction
Yes
No
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Specificity
• Specificity 85.6%
COPD GP records
Yes No
Model prediction
Yes
No
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Ratios of patients with COPD
• Ratio relative to all R03 users
2009 2010 2011 2012
GP
22.7% 23.6% 23.7% 23.8%
Model 2010
26.1% 27.5% 28.3% 29.0%
Model 2011
26.3% 27.7% 28.4% 29.1%
Model 2012
26.6% 28.0% 28.7% 29.4%
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Other models
• Diabetes Type I/II
• Heart failure
• High blood pressure
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Conclusions
• We created an useful model for estimating GPs indication for asthma or COPD.
• This model is used for market analysis for new drugs in the COPD/asthma market.
• COPD model based on age, gender, duration of R03 use and 5 different medication (group)s.
• Asthma model based on age, duration of R03 use and 5 different medication (group)s.
• Yearly update to keep accurate
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Questions
?
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