chris campbell, phd – senior consultant [email protected] non-compartmental analysis...

Chris Campbell, PhD – Senior [email protected]

Non-Compartmental Analysis using R

Chris Campbell, PhD


R as an Analysis Engine


Building Apps

• Java

• .NET

• R


Why Build Analytic Applications?

Why Build Analytic Applications on R?

Why Analytics?


Why Analytics?

• Analytics answer many questions

• I believe there is no company in the world today who cannot benefit from analytics in some way

• More and more people are realising it


Who is a good driver? How do we win more games?What bonus should I pay?

Will someone like this? What’s the right dose?When might this break?


Why build Analytic Applications?

• 3 key reasons we see:

• To deploy analytical tools to decision makers

• To make an analysts life more efficient

• To add rigour to an analysts workflow


Deploying Analytics

• Adding analytics into a business process

more informed decisions

• Complex analytics shouldn’t be attempted by non-analysts

• Communication between the decision maker and the analyst


Deploying Analytics

• If we build an application which:• is easy for the decision maker to use

• contains the correct analysis to apply

• communicates analytical results in suitable manner

this leads to some major benefits!


Benefits for the Analyst

Benefits for the Decision Maker

No need to wait for information

Can perform “what if” analysis

Decision not dependent on analyst availability

Less need to perform often-repetitive tasks

Comfortable that the “right” analysis is being

run

Can get on with more strategic things?


UserInterface

Data

AnalyticOutputs

DataStorage

AnalyticCode

Code Mgment

Analytic App Structure

AnalyticEngine


Why build Analytic Applications on R?

We want a programmable engine so that it can be readily extended (no black boxes please)

R can be extended by the developer as needed

We often want to be able to deploy new algorithms and techniques as they become available

R is rapidly developed


Why build Analytic Applications on R?

Building applications requires installing analytic engine on desktops, servers, clusters, clouds

R is license free

Building analytic applications involves integrating an analytic engine with other technologies (data sources, UI etc.)

R’s open nature means it can be readily integrated


Formal R Development

• Creating sophisticated analytic applications requires a formal development approach

• This mostly means taking standard development practices and applying it to analytics

• Mango’s formal R development procedures and structure has been evolving since its inception ~2004


Project Mgment Requirements

Behaviour Driven

Code Review

Review board

StatET

testthatroxygen2

Continuous Integration

Issue TrackingQuality Manual

Dev Procedures

Coding Standards

Knowledge Mgment

TestCoverage


RapidNCA, the non-compartmental analysis workflow tool

• Need for RapidNCA• Using .NET• RapidNCA Structure• Code Quality• Connections with R.NET• Complete & Deploy RapidNCA


Need for RapidNCA

• Customer needed to send monthly reports to dozens of trial centres

• Small team, so time limited• Predefined non-compartmental

analysis• Standardized report


Using .NET What is .NET?

• Object-oriented environment to develop applications

• Safe execution environment• Choice of programming languages• Framework consisting of:

• runtime• class library

• Developed with Visual Studio


Using .NET Visual Studio

• A graphical programming tool (IDE)• Visual Studio Express - free version


Using .NETChoice of languages

• C# is the main one• F# is a functional language (similar concepts to OCaml)• XAML (a Microsoft declarative XML language) for

interactive graphics• C++/CLI useful for legacy and bespoke parallel

processing (including GPGPU)

• Other possibilities...• Vb.Net is very like C# (no advantage over it)• Third parties have added languages to the CLI platform


Using .NET“Ajar Source” Platform

• Not exactly open source, but…• Most CLI third party languages are open• C# and VB.Net are not, but many open source

projects based on them• Microsoft have made F# open source• Compiler is free • Other editors / IDEs are available


Using .NETPerformance

• Performance is very good• On graphics (millions of data points will plot with ease

and zoom smoothly)• Computation is fast enough in C#, calling R adds little

overhead• Standard Maths library is limited; third parties and MS

maths for “drawing” are better• Data parallel computation is possible on the desktop

(GPGPU)• F# provides further “big data” capabilities


UserInterface

Data

AnalyticOutputs

DataStorage

AnalyticCode

Code Mgment

Data Service

RapidNCA Structure

AnalyticEngine


RapidNCA StructureMangoNca Analytic Code

AnalyseElement

DoAnalysis

GetAnalysis

UnitTests

DataChecks


RapidNCA StructureMangoNca Analytic Code


Code QualityUnit Tests

• Ensure product works!• User/Customer/Payer trust• Ease of maintenance/extension


Code QualityRun Code, Check Output

• Working Cases

> test1 <- ncaAnalysis(Conc = c(4, 9, 8, 6, 4:1, 1), + Time = 0:8, Dose = 100, Dof = 2) > checkEquals(test1[1, "ROutput_adjr2"], 0.9714937901,+ tol = 1e-8)[1] TRUE

> require(RUnit) > # there are other automated test packages!


Code QualityError Case Unit Tests

Use try

Handled Error Cases

> test7 <- try(AUCLast(Conc = 1:10, Time = 9:0), + silent = TRUE)> checkEquals(test7, + "Error in checkOrderedVector(Time, ... ")[1] TRUE

> test26 <- ncaAnalysis(Conc = c(4, 9, 8, 6, 4:1, 1),+ Time = 0:8, Dof = 1)> checkEquals(test26[, "ROutput_Error"], + "Error in checkSingleNumeric(Dose, ... ")[1] TRUE


Connections with R.NET

• What will be provided to R?• What will be returned from R?• What happens if something goes wrong?


Connections with R.NETUsing the R Service

• R.NET allows R calls to be submitted to an R service

• R.NET connects to R down to Expression level

• Data from return objects passed back into .NET


Connections with R.NETData Checks

• Function may be passed data outside its anticipated structure

> checkOrderedVector(c(0, 1, 3, 2, 4), + description = "Time") Error in checkOrderedVector(c(0, 1, 3, 2, 4), description = "Time") : Error: Time is not ordered. Actual value is 0 1 3 2 4>


Connections with R.NETData Checks

• The tool expects a certain return object• An error in an R call should be trapped by

the communicating function

• Return object passed as normal• An error checking element of the return

object can report information about the error

> check01 <- try(checkOrderedVector(Time, + description = "Time"), silent = TRUE)> if (is(check01, "try-error")) { return(object) }



• R is efficiently accessed, via R.Net (as pictured in Visual Studio) via a Plugin (as above)

_pluginsManager = new RPluginManager(PluginLocation, RLocation);_pluginsManager.SetActivePlugin();_session = _pluginsManager.GetSession();bool sessionOk = _pluginsManager.TryMakeSession(out _session);



UserInterface

Data

AnalyticOutputs

DataStorage

AnalyticCode

Code Mgment

AnalyticEngine

Data Service

R.NET

AnalyticEngine


AnalysisDisplay

Get PK Params

DataService

DialogService

AppLogger

Status BarService

App Config

Mgment

DataImporters

ProjectWizard

Validators

Receive R

Output

Create R Expressns

Connections with R.NET.NET Data Service

R.NET


Connections with R.NETUsing the framework

_pluginsManager = new RPluginManager(PluginLocation, RLocation);_pluginsManager.SetActivePlugin();_session = _pluginsManager.GetSession();bool sessionOk = _pluginsManager.TryMakeSession(out _session);

_session.SetNumericSymbol("TimePtVector", CheckTimePointData(toAnalyse));_session.SetNumericSymbol("ConcVector", CheckConcentrationPointData(toAnalyse));

var evalString = string.Format("ncaAnalysis(TimePtVector, ConcVector, …

MathEngineDataRowDto<double> ncaGetBack = _session.PerformNumericEvaluation(evalString, "ROutput_Error");_lastErrors = ncaGetBack.ErrorStrings;

_session.FlushConsole();_pluginsManager.RelinquishSession();


Complete & Deploy RapidNCA

• Can users understand how to use tool?• How confident are we in tool output?

• On-going code review• Independent test team• Installation Qualification• Operational Qualification• Performance Qualification


Deploy Tool


Data Import


Map Variables


Review Analysis


Review Grouping


Generate Report


Select Report Type


Add Group Comments


View Report


Conclusions

• Great graphical interfaces can be built using .NET

• Intuitive interactive features are available• R.NET allows R analysis to be accessed as a

service• Good coding practice will ensure application is

robust• Work on a well engineered framework will be

rewarded with desktop solutions created at high speed

chris campbell, phd – senior consultant [email protected] non-compartmental analysis...

Documents

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