1. The best of both worlds Pharma R&D IT - Informatics Rudi Verbeeck Guided analytics in the hands of the SME

2. Outline Changing world –More data –More analysis requirements Two worlds –Technical skills –Subject matter expertise Best of both worlds –Exploratory analysis: DIAN study –Guided analytics: phase III clinical trial planning October 2014 Pharma R&D IT Informatics2

3. Evolution of data In the pharmaceutical industry October 2014Pharma R&D IT Informatics3 1990 20002010

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5. Data challenges for the EMIF consortium Need for efficiency enhancing workflows October 2014Pharma R&D IT Informatics5

6. Skills – two worlds October 2014Pharma R&D IT Informatics6 Technical skills Domain expertise

7. PersonalPeerPublication Data analysis & exporation DiscussionsCommunication Handshake – packaging your analysis October 2014Pharma R&D IT Informatics7 Graphs that allow a rich analysis may not convey a message very well (and vice versa)

8. Example: circadian rhythm gene expression October 2014Pharma R&D IT Informatics8 Expression profile of typical genes

9. Exploratory analysis: data interactions Overview ⇨ zoom & filter ⇨ details on demand –A table view of the raw data gives an idea of the variables and values –Create overview visualizations, investigate distributions and correlations –Use filtering to look at subsets of the data –Detailed inspection of groups, outliers, anomalies... Brushing & linking show interactions between variables October 2014Pharma R&D IT Informatics9

10. Example: DIAN study DIAN = Dominantly Inherited Alzheimer Network Observational study of genetically inherited early onset Alzheimer’s disease –73 families –Mutations in amyloid precursor protein, presenilin 1, 2 –Age at onset estimated from parent –> 600 variables per patient Which baseline measurements are correlated with onset of AD? October 2014Pharma R&D IT Informatics10

11. Data overview October 2014Pharma R&D IT Informatics11 Show raw data in a table view Overview of measurements and visits

12. Explore correlations between measurements October 2014Pharma R&D IT Informatics12 General overview of measurement groups Detailed correlation for selected variable(s) Individual observations

13. Validated statistics: guided analytics Guide the SME through a series of decisions Give freedom to explore, backtrack, what-if Ensure sound & consistent statistical methods October 2014Pharma R&D IT Informatics13

14. Example: clinical phase III trial planning What patient inclusion criteria should be used for the Phase III trial of an AD compound? –We expect the compound to work best in patients with mild cognitive impairment (MCI), who are on the verge of converting to Alzheimer (AD) –We expect the compound to take some time to show clinical effect. We therefore want subjects not to convert early in the trial. –Which measurements are realistic in a trial selection setting? –Data from ADNI study (Alzheimer’s Disease Neuroimaging Initiative) October 2014Pharma R&D IT Informatics14

15. Guided analytics In a guided analysis, advanced statistics are packaged into a wizard-like application to guide the subject matter expert through a decision process 1.How is MCI to AD conversion measured? 2.What timeframe corresponds to “early” conversion? 3.Which (combination of) baseline measurements are predictive for early vs. late conversion? Use logistic regression modelling 4.For selected baseline measures, what cut-off value should be used as a selection criterion? Use survival analysis October 2014Pharma R&D IT Informatics15

16. Step 1: define early conversion October 2014Pharma R&D IT Informatics16 Text fields explain decision steps Select visit that separates early from late convertors Select conversion criterion (change in diagnosis or change in clinical dementia rating) Graph shows number of early / late convertors

17. Step 2: find predictive covariates October 2014Pharma R&D IT Informatics17 Select baseline covariates Verify that groups are balanced Verify variables are uncorrelated Calculate logistic regression model in R Verify model diagnostics Find significant covariates by p-value Inspect ROC for full model, stepwise model and cross validation

18. Step 3: find cut-off values October 2014Pharma R&D IT Informatics18 Select significant covariates from previous step Verify distribution by conversion group and determine cut-off Enter cut-off value Verify time evolution of cut-off groups Kaplan-Meier plot of conversion rates by cut-off group (calculate in R)

19. Conclusions Usage patterns should be supported by applications or licensing model. For example, using Spotfire –Informatician / biostatistician prepares guided analysis using full client –SME follows prepared analysis to draw conclusions using WebPlayer Think about how you present the data. Your chart encoding should be easy to understand. Conclusions from a guided analysis still need to be confirmed with a statistician. Guided analytics is a good communication tool. Distribute workload. October 2014Pharma R&D IT Informatics19