Statistical Activities

Statistical activities refer to selection of study design including sample size determination.


Our first eCRF was implemented in 2006 and since then the eCRF concept became a fully 21 CFR Part 11 compliant solution.

Data Management

Data management covers design of clinical trial database (following CDASH standards as possible).

Reimbursement of Patient Travel Costs

A simple but complete web-based service to fully cover the reimbursement procedure from collection of the proofs of the unique cost items through setting-up the study and visit specific condition till the providing the proof of payment for the investigators or to the patients.

eCRF System

Electronic Case Report Form (eCRF) is – generally – a web-based solution for data collection in clinical trials.
Our solution has already a history of 10 years: during this period we implemented functionalities from 21 CFR Part 11 compliance to flexible reporting.

Medical Information System

AE-related information should be carefully recorded, followed-up and potentially reporting to the Authorities within the presribed deadlines. Our web-based solution provides a perfect tool to collect information independently from the information channel (mail, telephone, web-page, Facebook, personally given information) and to keep everything together.

Reimburesement of Patient Travel Cost

Reimbursement of patient travels in clinical trials should be supported in a transparent way towards sponsors, investigators and patients. A perfect tool would support data entry (input) and reporting (output) at the same time.

Automated notifications

There are several situation when a company would like or has to follow an information channel to gain updated information without any delay to make a response.

A good example for this is the monitoring of Facebook accounts and company websites with respect of spontaneous adverse event reporting (by users/readers)

CRM System

A Customer Relationship Management (CRM) system can be totally generally and can be adjusted to serve a specific area/company. Our solution is to support a complex operational work of a company organizing post-marketing, educational studies.


Ability for electronically supported learning/training is already a must in many areas. Patient education or SOP-trainings can be easily managed and completed by electronic systems.

We concentrated to provide maximal flexibility in our solution.


The Clinical Data Acquisition Standards Harmonization (CDASH) model standardizes the way data is collected to facilitate the generation of SDTM tables.

As the primary aim of following CDASH Guidance is to make easier and more comfortable to transform raw study data into SDTM domains, CDASH is taken into consideration during CRF (and eCRF) design.


Information sharing on the content of SDTM in a more or less human-readable manner led to introduction of Define.xml, which – in short – contains all SDTM data AND metadata in XML-format.
The Standard content of define.XML is: Data Metadata (TOC), Variable Metadata, Variable Value Level Metadata, (Computational Algorithms), Controlled Terminology/Code Lists, Annotated CRF, Optional: Supplemental Data Definition Document.


Preparation of SDTM domains is quite a well defined procedure, which primarily means the proper application of the guidance in case of the standard variables and domains (e.g. sex, age or blood pressure). Due to the nature of SDTM even the study or therapeutic area specific variables can easily been transformed into SDTM-compliant variables with following the naming and formatting conventions.


Tables, Listings and Graphs are defined in the Statistical Analysis Plan with the help of Table Shells. Theoretically table shells defines the outputs with character precision (in case of Tables and Listings). Planimeter developed a highly sophisticated SAS-macro set which is able to deliver the required outputs with very high flexibility. What is the best in our solution that the macro set can be adjusted through an interface which does not require programing knowledge.


SDTM and ADaM data sets are programmed according to the associated specifications, and validated against a series of electronic integrity checks to ensure compliance to the models.

Additional QC includes independent verification of results. In the Subject-Level Analysis Dataset (ADSL) only one record is created for all subjects.

Quality Assurance Procedures

Steps establishing quality during development phase: Application of programing conventions, Commenting and segmenting, Application of continuously developed macros in TLG-derivation, Double programming on demand, TLG-derivation in a three-stage method: development, testing, production, Complete and transparent documentation of the whole activity (code development and QA), Continuous development of standardization according to international standards and guidelines.

Data Management

The concept of data management is relatively simple. Firms need access to high-quality, relevant data, provided in a timely and cost-effective manner.
Access to accurate data is necessary for effective investment decisions, trade execution, securities pricing, risk management, regulatory compliance, and portfolio valuation and measurement.

Data Reporting

Companies of all sizes realize the tremendous potential for data, but converting the data into actionable intelligence can be a challenge.
This is why organizations are looking to implement predictive analytics techniques to improve opportunities for growth, innovation, and gain a competitive advantage.