Automation of Data Conversion™

Automation of data conversion
made easy

Our experts simplify the data transformation process, standardize data goals, ensure consistency of attributes, define files and programming specifications, and avoid wastage of time and resources. Our tool also supports SDTM conversion and meets the requirements described in its Implementation Guide and internal standards.

The SDTM and ADaM

The SDTM (Study Data Tabulation Model) and ADaM (Analysis Data Model) are standards for human clinical trials (research) data tables and non-clinical data tables submitted by researchers to health authorities. The Federation of Clinical data Exchange Standards (CDISC) developed this specification.

ApoGI is AI and meta-data driven solution designed for automation of the end-to-end drug development process. Its data transformation feature automates the generation of datasets in various standard structures like SDTM+/-, SDTM, ADaM, or any other data standard as defined by regulatory authorities. 

The primary objective of this platform is to enable SMEs to focus on the curation of metadata needed to guide the automated production of artefacts, generated repetitively as part of Clinical Study Lifecycle.

This focus consequently enhances the value of the metadata and related transformations for SDTM and ADaM, making it possible to extend the capabilities of the platform to cover additional artefacts, processes, and use cases.

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Conversion Process

Our experts simplify the process of converting from SDTM/ADaM to NDA electronic submission, standardize data goals, ensure consistency of attributes for each variable between data sets, define files and programming specifications, and avoid wasting time and resources to verify late consistency.

The programming specification document is a key part of the SDTM conversion process because of its programming and verification capabilities, writing definition PDFs, and defining the generation of the main parts of the XML. There is a great need for a cost-effective way to ensure SDTM conversion meets the requirements described in the SDTM Implementation Guide and internal standards, and to ensure consistency between SDTM data sets, programming specifications, and definition files.


Our SDTM services cover a range of clinical and non-clinical experimental variables with programming specifications and define files. Consistency among them is guaranteed. It prevents the waste of resources due to consistency checking. If there are some changes in the SDTM dataset at any stage, we can update the data in the SDTM.

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Mapping datasets

Mapping datasets, e.g., raw to SDTM (or SDTM to ADaM), is a time-consuming task supported by this automation platform. With AI-enabled technology, automapping (or automated generation of dataset specifications to get from source to target) is possible. Additionally, it also becomes increasingly intelligent via guided, supervised learning with use over time, reducing time to generation as well as the QC cycles required. The platform has considered functionalities to automate the process of generating both ADaM and TFLs platform macros and transformational metadata to support ETL-like conversion

processes. Every step of the conversion process is traceable and documented.

Our tool also supports SDTM conversion with your original (source) datasets into SDTM datasets. We do this by using the SDTM mappings and templates within the tool. The tool allows us to do at various stages, from study start to submission. SDTM dataset creation during the clinical trial lets you check your data integrity – as soon as data becomes available.

Data transformation is simple too.

You are already mapping data at the study metadata level. Now you can run conversions automatically as soon as your trial data is available. You can automatically download the data in SAS Transport v5 (.xpt), dataset-XML, and CSV formats. Logs and audit records are also available as evidence of the successfully executed  transformation.

The platform considers functionalities to automate the process of generating both ADaM and TFLs platform macros and transformational metadata to support an ETL-like conversion processes.

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