To help users navigate PmWebSpec, we have provided several examples that cover the different features of the application. These examples include the development of dataset specifications, from creation to approval, preparing for e-Submission (e-Sub), downloading dataset specifications, generating SAS code, and modifying templates.

Example 1: Dataset Specification Lifecycle/ManagementStep 1a: Create a Dataset Specification from the PPK-CDISC Template

To generate a new dataset specification using a pre-populated template, users can choose the “Create New” feature available on the home page. Users are prompted to select a template from the drop-down list.

Once the PPK-CDISC template is selected, the dataset specification page will appear, pre-populated with dataset attributes in the Specification Information, variables and their attributes in the Dataset Structure, and derivations and flags in the Derivations from the CDISC ADaM popPK IG.

Once users fill out the required information in the specification, they can submit it. Upon submission, it will be assigned a specification ID and labeled as version 1. The specification can be further revised, as needed, in “Modify” (step 2).

This feature is often used by pharmacometricians when working with a new compound, a new indication, or a new type of analysis, where no existing dataset specification is available.

Step 1b: Create a Dataset Specification from an Existing One

If there is already a similar specification available, the “Import Existing” feature can be used to create a new one. Users are directed to a page containing a set of filters and search results (all results are displayed, by default). Users can filter by specification ID, compound name, dataset type, created by, modified by, and indication to find the desired dataset specification (Fig. 4).

Fig. 4

Filters in the search capability of PmWebSpec. Dataset specifications are filtered by dataset type “PPK-CDISC” and results are shown

Once the specification ID is selected, users will be prompted to choose a version to proceed to the dataset specification. This page will have all the information pre-populated from the existing dataset specification, except for the project description and paths, as these details may not be the same. Users can make modifications as necessary to all sections of the specification, including modifications to the Dataset Structure table, shown in Fig. 2 and the Derivations table in Fig. 3. After completing and submitting, it will be assigned a specification ID and default to version 1.

The benefit of using this option is that it allows users to reuse a dataset specification that already exists for a similar analysis. This saves time and effort in customizing a new specification from scratch. This feature is particularly useful when pooling a new dataset with an existing one, as it ensures that both datasets have a similar dataset structure and are developed using the same rules.

Step 2: Modifying a Dataset Specification

To update a dataset specification, users can use the “Modify” feature. This feature will direct them to the same page as shown in Fig. 4, with the exception that the approved dataset specifications will not be displayed in the results. Users can use the same filters to select a specification and its version, which will lead them to the dataset specification.

When modifying a dataset specification, the page will appear similar to the one in step 1. However, there are a couple of differences. Firstly, the specification information section will include fields to record the changes made and the person who is making the change. Secondly, users have the option to save their progress, even if the page is only partially completed. It is important to note that when a dataset specification is being modified, it is locked to prevent other users from making changes simultaneously. This helps prevent any potential loss of information due to conflicts. The lock will be released when the dataset specification is submitted.

Users use this feature to update dataset specifications, including variables and their attributes and derivations. It is common that there are multiple updates to a dataset specification before finalizing it. This application maintains a version history of all modifications made to dataset specifications, ensuring transparency and traceability during the dataset specification development. It provides an option to retrieve previous versions if necessary, offering flexibility in managing the dataset specifications.

Step 3: Review/Approve a Dataset Specification

The “Review/Approve” feature provides functions that allow users to view the dataset specifications as a complete document, both during and after the dataset specification development. It is useful when users need to look up information or perform QC checks. Users can search for the dataset specification using the same filters mentioned in the previous steps. It opens an HTML page displaying all the contents from the dataset specification. Users also have the option to view it as a PDF document. Once the dataset specification is finalized, pharmacometricians can sign off on the document using the signature panel located at the bottom of the page. When the dataset specification is approved, no further modifications are allowed.

Reviewing and approving dataset specifications is crucial because it allows pharmacometricians and programmers to align on the final version of the specifications, considering various aspects of dataset creation such as source data usage, derivation methods, and imputation rules, prior to finalizing the dataset.

Example 2: Exporting a Dataset Specification for e-Sub Preparation

The “Export eSub” feature enables users to convert dataset specifications into eCTD compliant data definition file format including variable name, label, type, codes, and comments (7). To access this function, users can select the “Export eSub” feature and will be prompted to select a specification ID. The e-Sub dataset specification will be displayed on the page (Fig. 5). Within this page, users can update the dataset label, variable name, and attributes. Additionally, they can modify variable order or add/delete variables to match the dataset before exporting the data definition file.

Fig. 5

E-Sub dataset specification page

Example 3: Downloading a Dataset Specification

Dataset specifications can be downloaded using the “Toolkit” feature on the home page. This will direct them to the same filters that were described earlier. Users can then choose the specification ID they desire and proceed to download the dataset specifications. Dataset specifications can be downloaded either locally to the desktop or to a server, in three formats: PDF, Word, and CSV. Dataset specifications in Word format can be appended to PMx reports, which help regulatory agencies in understanding the dataset creation process. PDF or Word dataset specifications can be shared with external partners for collaborations on dataset creation or analysis. Internally, we use the CSV dataset specifications to automate the QC process of the analysis dataset.

Example 4: Generating SAS Code from a Dataset Specification

The “Toolkit” feature includes an additional tool for automatically generating SAS code. Users can access this tool in the same manner as described in example 3. An example of SAS code is shown in Fig. 6.

Fig. 6

SAS code generated by PmWebSpec

During dataset preparation, programmers often spend significant time on tasks such as variable ordering and adding variables labels. This tool simplifies the process by extracting information from dataset specifications and generating SAS code. This code can be used to order variables, add variable labels, derive standard variables, round values, and impute missing values as necessary. By automating these tasks, programmers can save valuable time and focus on handling more complex algorithms and data issues. While the application currently provides SAS code, it can easily be translated to other programming languages. Additionally, future releases are planned to include the addition of R code.

Example 5: Modifying Built-in Templates and Derivations

The “Manage” feature includes a tool for template management. This application provides built-in templates that are designed to align with current practices. However, updates to the standards may be required to address study or project-specific issues. Maintaining up-to-date and user-friendly templates is crucial for all users. System administrators have the flexibility to modify these templates promptly after new standards become available, ensuring that new dataset specifications adhere to the latest standards without any delay.

To modify templates, system administrators can use the “Manage” feature and select “Modify Template”. Modifications can be made to existing flags and variables, such as adding or removing variables or flags, modifying the variable attributes and notes, and modifying notes and comments for flags. Users can also choose “Update Derivation” to add, remove, or modify derivation formulas.