Knowledge Center

Knowledge Center
Overview

Overview

Knowledge Center

All businesses have access to an extensive pool of knowledge - whether this is their understanding of customers' needs and the business environment or the skills and experience of staff. The way a business gathers shares and exploits this knowledge can be central to its ability to develop successfully. This doesn't just apply to huge multinational companies. Knowledge management can benefit everyone from a local news agent to a manufacturing firm. We at Kreara, do everything at our disposal to maintain the knowledge base that we have acquired over years. We use social networking technologies like blogs and websites to share information within the organisation and outside the organisation.

Case Studies

Case study-I

The title of the project was

Based on the raw data, the given study data structure is mapped to the targeted structure of CDISC SDTM with the aid of CRF and annotated CRF. The following steps were done to complete the tasks and delivered within the timeline:

The domains were decided based on the inputs provided by client in reference to the CDISC SDTM Implementation Guide (Version 3.1.1).

Each dataset was defined by metadata definition that provided information about the variables used in the dataset. The definition was described in a data definition document.

Each dataset variable in the document owes seven distinct metadata attributes:

The variable name (limited to 8-characters for compatibility with the SAS Transport format)
A descriptive variable label, using up to 40 characters, which should be unique for each variable in the dataset
The data type (e.g., whether the variable value is a character or numeric)
The set of controlled terminology for the value or the presentation format of the variable (Controlled terms or format)
The origin or source of each variable
The Role of the variable, which determines how the variable is used in the dataset.
Comments or other relevant information about the variable or its data.

In addition to these metadata attributes, another two attributes were also added in preparing datasets- one column indicating a note/algorithm relevant to use of each variable, and another to indicate how a variable is classified as a CDISC core variable.

Based on this document, codes were developed according to the coding standards followed by the client and thus analysis dataset was created. The coding was done such that the programs could be run on different operating systems. PROC SQL was used mainly.

Macros for conversion of date and time to CDISC date and time and study day calculation were generated.

Internal QC was performed for the code and the output datasets

Parallel Programming
- Outputs datasets were reviewed by the QC personnel through parallel programming. In this process QC personnel generated datasets independently and these datasets were compared with the datasets generated by the SAS programmers.
Code Review
- This involved review of the coding standards and the quality of the data generated. This also included peer review.

Case study-II

In this study based on the raw data and CRF, the given raw datasets were mapped to the targeted structure of SDTM and ADaM.

The following steps were taken for the conversion of the raw data to CDISC SDTM structure based on the raw data, protocol and CRF/annotated CRF.

The domains were decided based on the inputs provided by client in reference to the CDISC SDTM Implementation Guide (Version 3.1.2).

Each dataset was defined by metadata definition that provided information about the variables used in the dataset. The definition was described in a data definition document.

Each dataset variable in the document owes seven distinct metadata attributes:

The Variable Name (limited to 8-characters for compatibility with the SAS Transport format)
A descriptive Variable Label, using up to 40 characters, which should be unique for each variable in the dataset
The data Type (e.g., whether the variable value is a character or numeric)
The set of controlled terminology for the value or the presentation format of the variable (Controlled Terms or Format)
The Origin or source of each variable
The Role of the variable, which determines how the variable is used in the dataset.
Comments or other relevant information about the variable or its data.

In addition to these metadata attributes, another an attributes was also added in preparing datasets- one column indicating a note/algorithm relevant to use of each variable.

Based on this document, codes were developed according to the coding standards followed by the client and thus analysis dataset was created.

Macros for conversion of date and time to CDISC date and time; study day calculation and other required macros were generated.

Based on this document, codes were developed according to the coding standards followed by the client and thus analysis dataset was created.

The issues that surfaced during the course of project was escalated to the client and a discussion on the same was performed wither through mail or telecon.

Internal QC via code review was performed for the code and the output datasets by the QC personnel. This involved review of the coding standards and the quality of the data generated. This also included peer review. The peer review was done wherever required.

After finalization by the SAS programmer, a sanity check and the quality check was performed by the team leader. The final datasets and code and specification document was sent to the client.

The following steps were taken for the conversion of the raw data to CDISC ADaM structure based on the raw data, protocol, CRF/annotated CRF and SAP.

The domains were decided based on the inputs provided by client in reference to the CDISC ADaM Implementation Guide (Version 1.2) and SDTMIG version 3.1.2.

Similar procedure is carried out for ADaM domain as done for CDISC SDTM domain.