The TFL Workbench: From Pilot to Production

The TFL Workbench: From Pilot to Production
PhUSE 2017
Paper AD05
The TFL Workbench: From Pilot to Production
Iain Humphreys, PRA Health Sciences, Reading, UK
Hansjörg Frenzel, PRA Health Sciences, Mannheim, Germany
ABSTRACT
In 2016 PRA presented the TFL Workbench, a tool to standardise and accelerate table, figure, and listing (TFL)
programming. Comprising a user front-end for configuring output metadata; the ability to auto-generate reporting
code; and a suite of validated SAS® macros, statistical programmers can quickly adapt pre-configured table
templates for commonly occurring outputs and automatically generate code to produce the required output.
This paper builds on that previous presentation by describing the tasks that were necessary in taking the tool from
early proof-of-concept pilots to a production release. These include: facilitating study team working by developing a
better multi-user solution around a central database; formal validation of SAS macros; introducing standards
compliance reporting and metrics to gauge the extent to which standards are adopted at the study level; adapting
and developing standard operating processes to accommodate the new approach to TFL programming and quality
control; and establishing training and support structures.
INTRODUCTION
The TFL Workbench was developed to bring about efficiency gains and quality improvements in the production of
tables, figures and listings (TFLs), aiming to overcome some of the drawbacks associated with the traditional
programming process. The adoption of TFL standards was seen as the key to this, facilitating automation of those
parts of the process lending themselves to automation.
Described more fully elsewhere [1], the initial version of the TFL Workbench that was piloted consisted of:
• Extended TFL metadata, describing tables in a machine-readable and configurable format
• Suite of SAS ‘building block’ macros, each fulfilling a major function relating to table programming
• Code generation facility, capable of interpreting the metadata and writing a TFL program consisting of calls
to the relevant ‘building block’ macros
Early pilots were successful in proving the concept and confirmed that the tool merited further development. Often in
this industry, these developments are led by the biometrics departments who will also be the users of the solution,
and utilising tools with which the department is familiar, predominantly SAS and Microsoft Excel®. Yet such initiatives
are solutions to business problems which require a broader view on their implementation. As a business investment,
there are additional stakeholders and the need to engage with other interested parties (e.g. IT, Quality Assurance,
Business Operations). It is likely that sooner or later other languages and technologies may be required, and system
design is clearly important in developing a sufficiently robust and scalable solution. Consideration of how the new
solution will fit with existing platforms and processes is needed. Attention needs to be paid to operational issues:
how the user base will be trained and supported; also to how software will be versioned, deployed, tracked and
maintained. Some measures of the impact of the new solution need to be put in place.
These various aspects demand additional time and resource, and in many ways extend the way solutions are
traditionally implemented in many biometrics departments. This paper takes five such aspects that needed to be
considered in moving the TFL Workbench from its initial proof-of-concept to the first production release, and
examines them in more detail: system architecture; software validation; measurement of compliance; development of
standard operating processes; and user training and support.
EVOLUTION OF TFL WORKBENCH APPLICATION ARCHITECTURE
Early on during the design of the TFL Workbench, it became clear that in order to reduce complexity in any of its
components, we would need to split up the tool into two major parts:
•
•
A set of validated SAS macros that can produce tables, figures, and listings from analysis datasets.
Any software capable of storing configurations and writing simple text files.
By splitting up the TFL Workbench functionality into these two parts, we allowed each part to do what it is most
capable of: SAS Macros are good at generating SAS code flexibly, while a database is good at storing configuration
1
PhUSE 2017
data, and providing a structured and simple interface.
We started with the SAS part, by discussing how to split the functionality needed to create an output into the units
we called ‘building block’ macros, and decided which metadata needed to be passed to each of those macros, and
how we would structure them. It was important to discuss and agree these topics first, as they determined what the
database side would need to accomplish in terms of writing SAS code.
For the data storage side Excel was our choice for the first proof-of-concept application, since its tabular nature gave
us a very good interface to systematically define metadata for TFL output. After developing the proof-of-concept
application, we developed two more major iterations until we abandoned Excel as the sole tool for storing and
maintaining output configurations.
Although Excel is a good tool for organizing and manipulating configuration data, it is lacking in multi-user
functionality. Two main problems came up when moving from a single-user workbook to a shared, multi-user
workbook: in order to format the different spreadsheets efficiently, and to select and sort data, we relied heavily on
the tables feature within Excel. When adding new rows to a table, formatting and in-cell validation is propagated
automatically. Also, specific features of tables facilitate the selection and sorting of data, without the need to write
search or sort algorithms. Unfortunately, in a shared workbook, tables are not supported. Although we were able to
develop alternative solutions, none was very satisfactory. The breaking point however was reached when we
detected that we had to terminate shared mode in order to create custom templates (in particular, adding or deleting
worksheets). The problem was that we could not eliminate the risk of programmers losing work because they were
disconnected from the workbook while somebody else created a new template.
For a production version we decided to replace the multi-user workbook with a combination of an Access® database,
containing all configurations for a study and the ability to generate SAS code, and an Excel configuration workbook
template, which allowed programmers to ‘check out’ (download) configurations from the database, modify them to
adapt them for their study, and ‘check in’ (upload) the configurations again. Users are encouraged to check in and
check out configuration workbooks often, in order to stay up-to-date with metadata that is shared between multiple
outputs. Using Excel in effect as the front end onto the database kept interface development to a minimum.
However, care was needed to minimise the risk that users could accidently overwrite other users’ or shared
configurations.
To mitigate the risk of programmers overwriting each other’s configurations, we developed
•
Incremental check-out/check-in
o to ensure that programmers only can work on outputs and templates they are assigned to work on.
The configuration workbook keeps track of the worksheets that are updated, and the TFL
Workbench only updates its tables with contents of updated worksheets.
o to limit the ability to delete configurations from the database. This makes it possible to update
configurations which are shared amongst multiple outputs, like population configurations or
treatment group configurations. If programmers need to change some of those configurations, e.g.
only the safety population configuration, they can delete all the other configurations in the
respective worksheet prior to check-in. The upload process then simply ignores them. Although
this does not completely reduce the risk of overwriting each other’s configurations, it reduces it
substantially. Good communication within the team and well thought through work assignments by
the lead are the keys of preventing damage.
2
PhUSE 2017
•
Complete check-out/check-in
o
for the purpose of cleaning up the database or restoring a previous version of the configurations
There remained one problem: if programmers use complete check-in with a workbook created via incremental
check-out, as a minimum this would delete all templates of outputs that are not assigned to them. We developed a
tracking feature for check-outs in order to prevent this from happening.
Retaining parts of the Excel based solution in the form of the configuration workbook template allowed us to re-use
some of its functionality and some of its VBA code. In addition, we migrated the code to write SAS programs from
the workbook to the database and adapted it to work with its table structure. This meant that we only needed to
develop code for importing or exporting data (i.e. checking in and out), plus some minor interface development.
Previously, when using Excel alone, we also could not come up with a good solution for distributing TFL standards to
our programming teams. Our goal was to have TFL standard configurations for PRA and sponsor TFL standards,
which study teams could download and use in their studies. An obvious solution would have been to keep a separate
copy of the Excel workbook for every standard. But this would require maintaining the VBA code and structure of
multiple standard data sources, implementing changes to the structure of the TFL Workbench or its code base in
every one of those.
In order to distribute sponsor standards, we set up an access-controlled server location, where we stored copies of
the current version of the TFL Workbench Access databases, each containing its own PRA or sponsor standard, and
entered the locations of these databases and the standards they contain into a separate tracking database. A
downloadable copy of the TFL Workbench database contains two tables which are directly linked to this database,
but omits all of the tables in which TFL configurations are stored. After a team downloads this copy of the TFL
workbench database and open it for the first time, they see a welcome screen requiring them to select a sponsor
standard. This selection is based on the contents of the linked tables. Once they click the ‘Retrieve Standard’ button,
all tables containing standard configurations are copied from the selected standard database. Once this step is
completed, programmers can use all the functionality of the TFL Workbench and start configuring outputs for their
study.
3
PhUSE 2017
In parallel to downloading standard configurations, the study instance of the TFL Workbench also adds an entry into
a tracking table containing each database downloaded by a team, providing us with information about its name and
location, the standard it contains, and its version. This allows us to easily find databases, and run metrics reports
across all studies using the TFL Workbench. It also allows us to introduce measures to prevent cross-copying of
configurations from one study to another, which helps to enforce the rule that every project should start from a fresh
copy of the standard configurations.
These linked tables allow us to keep the list of available standards up-to-date, without constantly updating the
downloadable TFL Workbench. Furthermore, we can now track usage of standards across all distributed databases,
and monitor which version of the TFL Workbench is used in different projects.
Most importantly, it allows us to develop code for only one Access database and one Excel based configuration
workbook. If changes are made to the configuration workbook, teams can simply download a new version and save it
in the same location as the study TFL Workbench, replacing the older version. If the Access database is updated,
the study teams can download a newer version, extract all configurations from their old database, and continue using
the newer version. These newer versions will still prevent teams from cross-copying configurations from other
studies. Finally, all standard databases are maintained by the Global Data Standards TFL team.
FORMAL VALIDATION OF TFL WORKBENCH SAS MACRO SUITE
With the decision to separate SAS macros from the database part of the TFL Workbench, we also separated the
validation of the SAS code creating tables, figures and listings from the TFL Workbench database.
User requirements for the TFL Workbench SAS macros were developed soon after we introduced its concept during
a meeting with programmers, programming managers, statisticians, and the Global Data Standards team in 2015.
Based on this input, we also developed the main functions of the tool alongside the requirements the tool has to
meet in order to be accepted by Statistical Programmers, the future users of the application:
•
•
•
•
•
•
Initialise the programming environment
Specify treatment columns, stratifications, and population counts
Retrieve the data to be analysed
Produce statistical summaries
Save report-ready data summaries and global macro variables values as permanent SAS datasets
Create RTF output
For most of these functions one ‘building block’ macro was developed. In the case of generating summary statistics,
this main function is implemented in the form of two building block macros: one summarizing continuous variables,
and one for categorical variables. The call to each of the building block macros is written by the TFL Workbench
database. Building block macros are modular, i.e. each building block macro is comprised of a set of sub-macros. To
date the TFL Workbench SAS macro catalog contains 32 macros.
PRA’s Quality Assurance work instruction and reference guide for SAS macro validation only asks for user
acceptance testing of any SAS macro. However during design and development of the building block macros and
their relationship with each other, it became obvious that there would be too many possible configurations, and too
many separate macros to validate them by simply following test cases derived from user acceptance testing, as they
were often too unspecific. One example is the user requirement to configure summary statistics for continuous
variables by usage of statistical keywords for PROC SUMMARY:
4
PhUSE 2017
"As a statistical programmer, I want to be able to configure the summary statistics presented in a table, by using
statistical keywords available from PROC SUMMARY. Summary statistics shall be presented with two or more
results concatenated, or separately one parameter in each row. "
With more than thirty statistical keywords available in PROC SUMMARY it is difficult to test all possible table
configurations for summarizing continuous variables. It would simply take too long, and the risks of missing
combinations of statistical keywords is too high. Thus, instead of creating all possible tables, we performed unit
testing of each macro in the TFL Workbench SAS macro suite to ensure that each performed according to its
specifications. After validating the individual macros, we performed user acceptance testing to test whether the user
requirements were fulfilled.
While writing the code for the TFL Workbench macro suite the author also produced documentation detailing the
macro functionality, what inputs are expected (either as parameters or data, or both), how parameter values are
validated, what the output is and how it is structured, and how global macro variables would control the process flow.
This documentation was sufficiently detailed to be used as input for developing validation steps. Following the macro
documentation, test data and expected outcomes (often also data) were developed, with the focus on making it easy
to visually inspect the results. We tailored the test data to the functionality of the tested macro, in order to make it
easy for the tester as well as for auditors to decide whether the macro was working correctly. This also allowed us to
test the units (macros) independently, ensuring that interactions between macros were tested explicitly. During the
planning of the validation we carefully ensured that macros were validated in the appropriate order, i.e. all macros
which are called by other macros are validated before the calling macros. All details were documented in a validation
plan for unit testing. After the documentation was completed it was reviewed by the macro author to ensure that the
test developer had understood the function of the macros correctly.
Although all macros can be tested by a tester visually reviewing the results, we decided to automate the testing, to
avoid human error and increase efficiency. Following the validation plan, SAS programs were written which generate
the input data as well as the expected output data, contain the planned macro calls, and compare the macro output
with the expected output using PROC COMPARE. In addition, input validation of the macro parameters was also
tested, and the results were read from the log files produced by the testing programs.
Usually the number of expected output states of the macros were countable and small, thus it was easy to manually
configure the different alternative macro calls to produce all expected outputs. The macro producing summary
statistics for continuous variables and returning them in the order in which statistical keywords were configured was
a clear exception. The documentation for PROC MEANS and PROC SUMMARY alone list 34 different keywords (not
counting synonyms for statistics, or all of the possible percentiles). In addition to these 34 keywords we introduced
custom keywords in the TFL Workbench to allow calculation of statistics based on the geometric mean, a request
made by our statisticians.
The aim of the validation of this macro was not to validate PROC SUMMARY, but rather to test whether the macro
would return the results reliably, independent of the sequence in which the statistical keywords were assigned to its
parameters. It is not possible to efficiently test all possible combinations for over thirty statistical parameters – each
would need to run at least one PROC SUMMARY, which would require a tremendous amount of time and resources
on most SAS infrastructure platforms; also it seems to be impossible in SAS to calculate all possible permutations of
30+ values (>2.65 * 1032 permutations) efficiently. We decided to limit testing to a minimum of 4 (24 permutations)
and a maximum of 9 (362,880 permutations) different, randomly selected statistical keywords. These numbers were
in the range of statistical parameters we usually configure for a summary table. We used CALL ALLPERM [2] to
create all permutations (CALL ALLPERM can handle up to 15 values (i.e. 1.31 * 1012 permutations)), in separate
batches depending on the number of statistical key words. To keep the run time short, we randomly selected 50
permutations from each batch, if the number of permutation exceeded 50, and used them to run our summary
statistics macro. In each batch the output of the first call of the macro was electronically compared to the output of
every other call to the macro, with the expectation that every call consistently returns the same results for each
statistical keyword, independent of the sequence of keywords. We used the return code of PROC COMPARE (stored
in automatic macro variable &SYSINFO) to determine whether the testing was successful or not, and printed a
success message, or the PROC COMPARE output in the case of a comparison failure. This kind of ‘brute force’
testing revealed problems in the code which had not been detected via code review or the manual configurations
used for earlier test steps.
As the individual macros were developed in a modular design, integration testing up to the level of the building block
macros was already done during the validation. With the proven confidence that each individual macro works to its
specifications, and within its building block macro, we continued with user acceptance testing.
The basis for developing test cases for user acceptance testing were the documented user requirements. To
5
PhUSE 2017
illustrate the process, one request was to initialize the programming environment to a known state before each
output is produced. This is important, as it prevents application settings or results from previously created outputs
being carried over to the next output, in the event where a program fails to execute correctly. The request translated
into two requirements:
4.1 Clean up the SAS Work Environment
As a Statistical Programmer, I want the SAS environment/work library to be devoid of any result datasets from
previous tables or column header variables/macro variables from the production of a previous table. I want everything
deleted that could lead to reporting incorrect results.
4.2 Reset Environment to a Well-Known State
All global macro variables should be reset to a known state, such that the macros of the TFL Workbench execute as
defined.
These are requirements that need to be tested with every test case, as they apply to each output created by the TFL
Workbench SAS Macros. Other requests can be implemented as unique test cases, as they refer to different output
types. Six test cases were developed, corresponding to the different types of tables we wanted to create:
•
•
•
•
•
•
Tables presenting subject counts
Tables for occurrence data
Tables for shift data from BDS datasets
Tables for categorical data from BDS datasets
Tables for continuous data from BDS datasets
Tables presenting statistics for both categorical and continuous data
To ensure that the macros performed as intended, we used real world clinical data for the testing. For the macro
catalog to pass for each of the six test cases, the user acceptance tester had to electronically compare the outcome
of programs written by the TFL Workbench (using building block macros) with the output of manually written
programs. With minor amendments to code, all TFL Workbench macros successfully passed validation.
COMPLIANCE REPORTING AND ASSOCIATED METRICS
MEASURING COMPLIANCE TO A STANDARD
One of the overarching aims of the TFL Workbench is to facilitate the adoption of standards for TFLs. As with any
standard there is the need to measure compliance: the extent to which the standard is being followed. This is
necessary for two principal purposes:
•
It aids the TFL QC process, by informing the study teams of the changes made at study level, indicating
where special attention should be paid during the review and verification of TFL configurations.
•
It permits the owners of the relevant standard to gauge its uptake and make decisions based on this. For
example, consistent deviation from a standard might highlight a lack of awareness and therefore a training
need; alternatively it might indicate that the standard itself requires amendment.
To meet these needs, a compliance report has been developed and implemented. The starting point for the
deployment of the TFL Workbench at individual study level is a copy of the Access database, populated with the
relevant standard configurations (sponsor or PRA). Once study-specific configuration changes have been made to
generate the outputs required for a study, the compliance report is run to compare the study instance of the
database with the standard database on which it was originally based.
The report was developed as a SAS macro, with sub-macro utilities to:
• read the Access databases and retrieve configuration details;
• make the comparisons and flag changes;
• report the configurations used on a study, and highlight the changes detected.
Since the compliance report plays an important role in the QC process (covered in more detail later), these macros
were subjected to formal validation in the form of user acceptance testing.
The actions of the compliance report are as follows: the name and location of the study-level TFL Workbench
database are passed to the primary macro. This database is queried to determine which standard is being followed,
and which template configurations have been used in the study reporting. Using this information, the macro locates
and retrieves the relevant ‘master’ configurations from the standard database, and performs a comparison of each
6
PhUSE 2017
individual item of metadata. In addition to comparing the individual table templates (each analogous to a unique
table shell), other TFL Workbench entities are compared, including treatment sets, population sets, pre- and postprocessing code snippets, and other settings. One of the benefits of using a database such as Access to house the
TFL metadata configurations is that this can be more simply and reliably read in SAS, compared with Excel (another
factor in TFL Workbench architecture decisions).
Changes that are detected between the standard and the study instance are classified as being merely cosmetic
(e.g. a change to a row label, or column header), or data-related (i.e. a change which would impact the numbers
being presented). These changes are stored as a series of flags and used in the subsequent reporting.
The entire TFL metadata for a study is written to the compliance report, which is in the form of an XML file, produced
via the EXCELXP ODS tagset.
An overview of the compliance reporting process is shown in the diagram below.
When the report is opened in Excel, the user can see a separate worksheet for each template used in the study,
depicting original standard configuration on the top half, followed by the study configuration on the bottom half, and
colour-coded differently. Any changes are highlighted in different colours in the study-level settings. The example
below shows the configuration for a concomitant medication summary which has been altered. The standard version
of the table specifies both preferred term and therapeutic class. The study summary presents only preferred term, so
the TFL Workbench class variables have been updated, and also the corresponding row labels, their header, and
level of indentation. A change to a class variable is categorised as data-related, and therefore colour-coded
differently to the other changes which are classified as cosmetic.
Colour-coding is achieved during the reporting step, using CALL DEFINE statements in PROC REPORT compute
blocks to set cell style attributes conditionally, according to the change flags previously derived. The standard
process for using the TFL Workbench requires a review of configurations (discussed more fully in a later section); by
highlighting changes the compliance report assists study teams, directing them to configurations that need additional
special attention.
7
PhUSE 2017
DERIVATION AND REPORTING OF METRICS
Whereas the study team will run the compliance report for their individual study, the Global Data Standards team
has the facility to run the same report over multiple studies’ TFL Workbench databases. When run in this mode, the
retrieved metadata and related change flags are stored permanently for each study. The main purpose of this is to
drive metrics reporting. The compliance data from individual studies are pooled, integrated with information from
other sources and converted to a comma-separated variable (CSV) file; this is periodically uploaded to a repository
in order to refresh a metrics dashboard.
The metrics dashboard is published to the PRA intranet and includes the following metrics in the displays:
Measure
Significance
Number and percent of tables produced by the TFL
Workbench, versus those traditionally programmed
A measure of coverage: the extent to which TFLs can
be produced by the TFL Workbench
Number and percent of TFL Workbench-produced
tables based on standard templates, versus those
based on custom templates
A measure of the degree to which the standard
templates met the study reporting requirements
Number and percent of TFL Workbench-produced
tables based on standard templates that are
unmodified, versus those with cosmetic changes,
versus those with data-related changes
A measure of the degree to which standard
templates needed study-specific amendment
Number of tables requiring pre-processing and postprocessing code
A measure of the need to overcome a constraint of
either the TFL Workbench or the source data
These metrics can be filtered by the study ID and by the standard being followed, to permit groupings to be
examined. Some examples from the metrics dashboard are shown in the panels that follow:
8
PhUSE 2017
Over time such metrics will play a role in the governance of TFL standards, alerting their custodians to any persistent
deviation from the standard, and facilitating a root cause analysis. Judgments can then be made about possible
corrective actions needed to ensure higher levels of future compliance.
PROCESS DEVELOPMENT
The TFL Workbench clearly represents a departure from traditional TFL programming practice, and standard
operating procedures (SOP) have had to be adapted accordingly. Since not all TFL outputs for a clinical study will be
produced by the TFL Workbench, existing processes were reviewed and expanded to incorporate the new practices.
This exercise was carried out in conjunction with the process owners, specialists and PRA’s Quality Assurance (QA)
department, who scrutinised every step suggested by the development team. The involvement of QA was essential
to ensure that adjacent processes were not adversely impacted, that standard industry practices were not violated,
that SOP standards were met, that an appropriate amount of documentation was available, and that the process
would withstand auditory inspection. Finally, in order to permit traditional TFL programming in conjunction with the
TFL Workbench, a new work instruction was drawn up to complement traditional SAS TFL programming.
Firstly a decision is taken by the Lead Statistical Programmer, based on comments from the Lead Biostatistician and
the annotated output shells, whether to produce a summary table with the TFL Workbench or not. If not, the
traditional TFL programming process is followed, and the rationale for this decision is documented.
For a table to be generated using the TFL Workbench, certain tasks are carried out by the primary programmer
allocated to produce the output. These tasks are analogous to the generic steps of:
•
choosing the correct population
•
specifying the correct set of treatment columns
•
selecting the correct records for analysis from the relevant dataset
•
performing frequency counts and percentage calculations on categorical data
•
producing summary statistics on continuous data
•
assembling a report-ready table dataset
•
reporting the output, with associated paging and titling considerations.
With the exception of the last of these steps, traditional table programming has been replaced by configuring the TFL
Workbench and using its code generation facility to produce a program. The starting point is always the standard
configurations which are then adapted to meet study-specific needs if necessary. Custom configurations can be
created in the absence of a standard template being available, for tables lending themselves to automation.
Changes are made according to the Statistical Analysis Plan (SAP), annotated TFL shells, and source data (e.g.
(ADaM) specifications. Although manual program coding tasks have largely been replaced by checking and
amendment of table configurations, a sound knowledge of the study and thorough understanding of the analysis
datasets is still required on the part of the primary programmer, as is the normal diligence in ensuring the table
meets the study requirements prior to releasing to QC.
Major changes are also apparent in the QC process: much of the independent double-programming that is carried
out when outputs are traditionally programmed has been replaced by review of TFL Workbench configurations by a
QC reviewer (a programmer other than the primary programmer). Using the compliance report, the settings are
9
PhUSE 2017
checked to verify that they are appropriate for each output. If cosmetic changes have been made, the formatting is
checked; if data-related amendments were made, the relevant variables, statistic names, where clauses are all
inspected for correctness. A few results are also spot checked, and the usual visual checks are still carried out to
verify that each output matches its shell.
Additionally, and regardless of method of production, all outputs undergo review by the lead programmer, lead
statistician and a senior statistical reviewer to ensure consistency in numbers between outputs, that all intended
outputs are present, and that the results make sense.
The ability to replace much of the need for independent double programming is a direct consequence of having
formally validated macros in the TFL Workbench, as described earlier.
As with changes to any standard processes, the documentation must be subjected to the formal QA approval
process, and implementation must be planned in alignment with the release cycles for such processes. The
communication of the process changes to the programming user base, and the availability of relevant training also
need to be planned accordingly.
TRAINING AND SUPPORT FOR THE USER BASE
Reaping the potential benefits of the TFL Workbench relies on the statistical programming community firstly adopting
it and then using it optimally. To help meet this aim, a number of materials have been prepared, using a variety of
media.
INITIAL TRAINING AND EDUCATION
The implementation of the TFL Workbench is ongoing on a standard-by-standard basis, as and when libraries of
different sponsor standards are configured and made available. When study teams are ready to start using the tool
for the first time, instructor-led training is carried out. This covers the following topics:
•
introduction and rationale for the TFL Workbench
•
initial set-up of a TFL Workbench database and the programming environment
•
how to auto-generate and run a TFL program with the TFL Workbench
•
how to check out configurations, make study-specific updates, and check them back in
•
how to run a compliance report
•
programming process changes
Delegates carry out hands-on exercises in a training area to give them direct experience. Training is timed to
coincide with the start of TFL programming. However, for teams using the TFL Workbench for the first time, Global
Data Standards will review the SAP and TFL shells and advise on any special considerations, including how data
might be arranged in ADaM for subsequent optimal use of the tool. This review would take place ahead of training, to
coincide with ADaM programming. We will also work with Lead Statistical Programmers who are first-time users, in
the initial set up of their study. In the future we plan to develop self-training modules, so that programmers can
access on-line training on demand.
SUPPORT STRUCTURES
Once programmers are trained and using the TFL Workbench, a number of resources are available to them:
• A User Guide has been developed. This contains all instructions needed for set-up; it also provides
information on the different TFL metadata entities: Table of Outputs (TOO), population sets, treatment sets,
study-level metadata, pre- and post-processing. Additional notes explain the standard template
configurations for commonly occurring output types. Finally, hints and tips for how to handle special
situations such as cross-over or other multiphase studies, or tables with adverse event rates.
• A TFL Workbench ‘Wiki’ is being developed with detailed information about each TFL standard that is
configured and available. It is intended to show example outputs resulting from standard templates.
• An intranet-based Issues and Enhancements tracker is available, in which problems can be reported,
questions asked, or ideas submitted. Automatic alerts to the Global Data Standards team ensure a timely
response to issues.
• A monthly User Forum is held, to which all TFL Workbench users are invited. Global Data Standards will
typically present a TFL Workbench-related topic, often to refresh or reinforce an aspect of the training; this
is followed by an open session in which attendees are encouraged to ask questions, exchange ideas and
give feedback.
As the user base grows we anticipate increased demand for such resources. To meet that demand early adopters of
each standard available in the TFL Workbench will become subject matter experts (SMEs) and assist in providing
support to their peers.
10
PhUSE 2017
CONCLUSION
The five topics discussed above were all, amongst others, pre-requisites to the successful deployment of the TFL
Workbench solution to study teams:
•
Providing a platform sufficiently robust to support a multi-user study team required an architectural change:
the migration from Excel to a database. This also facilitates the access to TFL metadata for other purposes
such as compliance reporting.
•
To achieve efficiency gains by reducing independent double programming required software to be formally
validated; in turn this permitted QC to be streamlined to a review of configurations and outputs.
•
The solution relies on the adoption of standards, since it is this that permits automation and its associated
benefits of deceased programming time and increased consistency. In order to monitor the uptake of
standards it was necessary to develop the compliance report and its related metrics.
•
The compliance report also plays a part in the QC process, which along with other standard operating
procedures needed to evolve to incorporate the usage of a new tool to produce TFLs.
•
Appropriate materials and support structures were needed, to help ensure optimal use of the TFL
Workbench and thereby achieve the maximum efficiency gains.
All of these topics form part of a broader applications development landscape, of which programming the solution is
just one part. As such they extend the horizons of the biostatistical programming function, sometimes requiring
additional resources, new approaches and possibly new skills also.
REFERENCES
[1] Humphreys, Iain and Frenzel, Hansjörg. 2016. “The TFL Workbench: Tools to Standardise and Accelerate Table,
Figure, and Listing (TFL) Programming”, PhUSE 2016, AD01
[2] Wicklin, Rick, "Generate all permutations in SAS", in: The Do Loop, Oct. 31, 2010:
http://blogs.sas.com/content/iml/2010/10/13/generate-all-permutations-in-sas.html
RECOMMENDED READING
A good description of how to use the value stored in &SYSINFO for efficient display of PROC COMPARE results is
provided in: Hinson, Joseph; Margaret Coughlin: "Deciphering PROC COMPARE Codes: The Use of the bAND
Function". SAS Global Forum 2012.
CONTACT INFORMATION
Your comments and questions are valued and encouraged. Contact the authors at:
Iain Humphreys
PRA Health Sciences (UK)
500 South Oak Way
Reading, RG2 6AD
Email: [email protected]
Web: www.prahs.com
Hansjörg Frenzel
PRA Health Sciences (Germany)
Gottlieb-Daimler-Strasse 10
Mannheim, D-68165
Email: [email protected]
Brand and product names are trademarks of their respective companies.
11
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertisement