Data Science Services Dirk Engfer Page 1 of 5

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1 Page 1 of 5 Services SAS programming Conform to CDISC SDTM and ADaM within clinical trials. Create textual outputs (tables, listings) and graphical output. Establish SAS macros for repetitive tasks and for centralizing code. Validation of programs. Review of the statistical analysis plan and adjustment to actual studies (esp. with respect to dataset specifications). Solutions to enhanced handling of study related code lists and metadata Write VBA code modules to automating features of Microsoft Excel workbooks and Word documents. For test projects, code can be written for OpenOffice as well. Metadata comprise e.g. output title, number, footnotes and are usually stored in spreadsheets. Code lists: The application that will handle code lists in a multi user team consists of spreadsheets, a Python script and a SQL database. Each user works on codes in a nonblocking fashion with permanent write access independent of other user's actions. Pragmatic quality assurance realization along with programming activities (creation of Python scripts is service) Quality assurance focusing on the header content of SAS programs is to be realized by Python programming. Example 1: Scripts iterating over your whole SAS program library check whether program headers follow standard conventions and notify the user if not. Completeness of project specific SAS files (programs,.lst,.log) is a matter to QA. Example 2: The number and status of project files in your production environment is to be observed and compared to a specification programmatically. Users will be notified on mismatches.

2 Page 2 of 5 Transformation of SDTM/ADaM datasets into standard CDISC Dataset-XML structure It is service to programmatically create XML from your SAS datasets/csv files. The transformation happens on the freelancer's site. The customer receives resulting output files. Solutions to transforming study aggregate counts programmatically to XML files (creation of Python scripts is service) Improve your production system with a Python code library being open to your team members as executable add-on scripts. These scripts enable you to instantly create XML files (for e.g. an upload to ClinicalTrials.gov, for other internal purposes, or just resulting in XHTML) - converted from either statistical aggregate counts or study related metadata. Modeling complex observational data with MongoDB (services for epidemiology, clinical studies, other data warehouses) Build up data models for your diverse input data (e.g. medical devices, epidemiological data, electronic health records). Optimize data models in terms of query performance. Write application code (Pymongo) to extract data from your database. Web based form creation (html) to submitting query parameters for routine data extracts. Translate HTTP requests which are received by the web framework Django, into valid MongoDB queries (i.e. coding so called view functions in Django is service). Set up application code: (1) Emit MongoDB documents JSON formatted and (2) create a web response to browse visualized data aggregates (i.e. setup of Django templates is service). Convert JSON documents into CSV format for further analysis with your statistical tools. Benefit Data analysis SAS in-depth knowledge: Fluently coding SAS data steps, SAS Base procedures and SAS macros relevant to common clinical study analysis patterns. I update myself continuously on latest SAS language enhancements and new features. Examples: ODS Graphics is my favorite way of creating graphical output. SAS Hash Object programming promise fast data processing. Strong interaction between SAS and Excel: You may benefit from my deep understanding of working with Excel and how to exchange data between SAS and Excel. Though the import of Excel spreadsheets was improved and simplified a lot in SAS, malicious values in Excel should be detected before one imports spreadsheet data. Aiming for clean data and with my VBA knowledge serving you, I can come up with professional Excel macro solutions that you just apply without the need of any VBA skills.

3 Page 3 of 5 Quality assurance applied on metadata: Practical work on clinical studies involve the appropriate way of handling output related metadata. The same holds true for status related information on all programming activities. In both cases, I can implement add-on check routines and perform the automated checks for you. Proof of concept: In the past, I could save a lot of time with (semi)automated check solutions compared to manual work. In addition, a high quality level can be reached at an early stage. SAS programming environment Depending on your versioning system, there might be optimization gaps. I would be pleased to help you wrapping project files in a rather strict frame or implementing script solutions to easily verify the correct project status. Goal could be a light-weight control system operating on your projects and which is used either through a GUI or on the command line (no programming effort and no knowledge in another scripting language is required by staff). Transform study related data to multiple formats: If Python is installed on your system then I can work out for you a concept of a XML creating environment. Basically, your SAS job writes to a couple of CSV files containing aggregate counts that you need to upload to ClinicalTrials.gov or which are needed for another XML destination. At this point, I can code and install scripts at your site that build up the desired XML structure. Thus, you are freed from manual and error prone work and your XML outputs contain always up to date information. If you are asked to provide JSON formatted data to another department, then I would be pleased to come up with script solutions that you simply run on the command line to do the transformation. JSON is a format used for web applications to visualize or exchange data. Data stores and data interchange Prepare yourself for upcoming data interchange necessities and for non-relational heterogeneous data: In a pilot project, I had set up a MongoDB database system on a Linux platform. Epidemiological data were successfully stored in there. With my experience gained throughout this project, I can enable your organization to serve/store complex data where a schema-less design is appropriate (consider various input data formats or data formats which change over time). I can also realize the import of such data structures for SAS analyses in turn. With my support, you catch up with technologies recently entering leading biotech companies. CDISC XML standards Dataset-XML: Exchange your data with other sites in a standard format (well-formed XML).

4 Page 4 of 5 Guidance on data science aspects Professional SAS programming tips esp. with regard to handling of metadata. Guide you on techniques for data interchange and implementation strategies (XML for ClinicalTrials.gov and for the FDA, JSON with MongoDB). Presentation and demonstration of how to store/model data with MongoDB. Advise you on pragmatic ways to control the project programming status, even under changing circumstances or in specific situations. Skills SAS: Base, Macro, ODS Graphics (Graph Template Language), SQL. Data Models: CDISC (SDTM, ADaM). Platforms: Windows, Unix, Linux. Programming languages: Python, Visual Basic for Applications, VBScript, JavaScript. Markup languages: HTML, XML (incl. XSLT). Web frameworks: Django. NoSQL databases: MongoDB. Office Suites: Microsoft (Excel, Word, Outlook), OpenOffice. Languages: German, English. Professional projects KOEHLER eclinical GmbH (12 months contract) SAS programmer In-house solutions Scripting for data scientists Weight management, Outcomes research (insulin) Statistical output creation, interactive data visualization. Submission datasets. CDISC Define-XML + ARM1.0. Independent projects XSLT for ARM metadata documentation. JavaScript library D3.js, client-side JavaScript, Perl scripts 2015 KOEHLER eclinical GmbH (12 months contract) Trial programmer: In-house solutions Scripting for data scientists Diabetes, phase 3A Creation of tables/listings/figures, documentation, review Independent projects Python (for XML/HTML), VBScript, D3.js (for interactive graphics) DATAMAP GmbH (contract) Upper limb spasticity, phase 3 Double programmer Programming SDTM datasets from annotated ecrf Production software in use: SAS 9, MS Excel

5 Page 5 of 5 Work experience Academic qualification: University of Kiel, degree in Nutritional Sciences and Home Economics (Dipl. oec. troph.) in Since 2002: SAS programming on phase I to phase III clinical studies. Since 2006: Extended clinical programming using Python, VBA and VBScript in addition to SAS programming. Build up template code libraries populated and applied on various projects with the desired effects as follows: (1) saving time and (2) facing the ability to control the adherence to standard procedures programmatically.