The best of both worlds

Pharma R&D IT - InformaticsRudi Verbeeck

Guided analytics in the hands of the SME

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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

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Evolution of data

In the pharmaceutical industry

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Largely paper based R&D

Computerised records,

increased efficiency

Rich electronic data sources, new linkages,

new applications

More data sources, more linkages, more

applications

1990 2000 2010

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Data challenges for the EMIF consortium

Need for efficiency enhancing workflows

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Diversity• Primary care• HIS• Administrative• Regional record linkage• Cohorts• Biobanks

Scale• 52M subject• 7 EU countries• 25K AD• 95K Metabolics

Privacy• Population data• Cohorts

Harmonization• Common data model• Semantic web technology

Analysis & research• Disease insights• Extreme phenotypes• Pharmaco-epidemiology

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Skills – two worlds

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Identify data sources

Preprocessing, QA, cleaning

(Exploratory) Analysis

Verify conclusions

Interpretation, insights Actions

Technicalskills

Domainexpertise

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Personal Peer Publication

Data analysis & exporation

Discussions Communication

Handshake – packaging your analysis

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Graphs that allow a rich analysismay not convey a message very well

(and vice versa)

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Example: circadian rhythm gene expression

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Expression profile of typical genes

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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

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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?

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Data overview

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Show raw data in a table view

Overview of measurements and

visits

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Explore correlations between measurements

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General overview of measurement groups

Detailed correlation for selected variable(s)

Individual observations

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Validated statistics: guided analytics

• Guide the SME through a series of decisions

• Give freedom to explore, backtrack, what-if

• Ensure sound & consistent statistical methods

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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)

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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

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Step 1: define early conversion

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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

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Step 2: find predictive covariates

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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

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Step 3: find cut-off values

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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)

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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.

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