Good Programming Practice Guidance
Published Guidance Document
Version 1 of the guidance has been published and is available as an MS Word document for adoption.
File:GPP Guidance Document v1.1.docx
We also have a one page poster summary of GPP principles for download. File:PP25.pdf
The guidance is open for public comment and comments will be incorporated in periodic revisions so please continue to review and comment on the wiki below
This document provides guidance for good programming practices (GPP) for data manipulation, analysis and reporting of clinical data in health and life sciences organizations.
GPP addresses the way in which you write code and comments. The following key principles of Good Programming Practice and are followed in this document.
- Write code that is clear and easy to read and review
- Write code that is easy to maintain, modify and debug
- Use robust programming to reduce the need for code maintenance
- Develop code that can be reused or easily adapted.
- Write code that uses minimal computer processing resource (Efficient code)
- Write code in such a way to reduce logical and syntax errors
This guidance is primarily aimed at SAS programmers, however the principles of GPP also apply to other languages such as R and Stata. In addition, although this is not produced with SAS macros in mind, the same principles apply to macros too.
We often have to update existing programs to add new rules, copy programs from one study to another, and take over programs written by others. This guideline aims to show how to produce well structured and well documented programs so that they are easy to read and maintain over time. It is meant to be applicable to all programs, and hence all programmers regardless of experience. Specific rules may be of more use to novice programmers, but applying the principles should be in mind for experienced programmers and mentors.
Getting Started With a New Project
When starting work on any new study, it is important to familiarize yourself with the study. Review the study documents and try to understand the following:
- The objectives of the study.
- How many patients will be enrolled, randomized and treated.
- Schedule of events, i.e. screening, run-in, treatment periods, washouts, how many treatments and when they are taken.
- What is the primary endpoint and how, when and where is this data collected.
- Timelines for the trial, when is the database lock, when should the top line results be ready, and when should all the reporting be finalized.
- The current status of the project.
Study documents include:
- Protocol (CSP) - study outline and statistical sections are usually of relevance.
- Case Report Form (CRF, annotated CRF) - aCRFs are annotated with the data set name and variable name, - to understand where the data comes from and how it was collected and where it is stored.
- Statistical Analysis Plan (SAP) – to see what data is reported and how
- Analysis Datasets (ADS) specifications – describes which derived datasets should be created and what will be stored within them, including detailed definitions of endpoints. Used for ADS programming and validation.
- Table shells – used for tables, listings and graphs (TLG) programming or validation.
- Publications, if available (to check against already available results).
- Previous Clinical Study Reports (CSR), if available (to check against already available results).
Before you start programming, it is important that you familiarize yourself with all the relevant company IT systems, standards, SOPs and Guidelines on both programming and program validation. All these should be adhered to.
- Familiarize yourself with the system you are working on.
- Check for company specific programming standards.
- Check for study– and project specific standards.
- Check for industry standards like CDISC which are to be applied or can be applied.
- Check if a similar project/study has been worked on, i.e. check if available SAS code can be reused.
- Check for project-independent macros that can be applied.
Now that you are ready to start programming, keep in mind some basic standards:
- Flow of data from raw data → tabulated data sets → analysis data sets → outputs.
- Do not derive anything in more than one place.
- All derivations should be implemented on ADS-level not during output programming.
- Structure your program to read in all external data at the top, do the processing, then produce any outputs or permanent analysis datasets.
- Keep in mind that you or somebody else might need to change your program in the future.
- Keep in mind that somebody will validate your program.
- Add comments as you are programming / do not plan to do that afterwards.
- Avoid data driven programming.
- Try to simplify your code to make it more readable and easy to perform source code review (e.g. do not make too many derivations in one data step, consider creating multiple data steps).
The language used in programming code and within headers and comments is English.
A standard header should be used for every program. The purpose of the header is to identify the program and provide documentation including revision history. It provides the necessary information to a code reviewer to identify and understand the program and its development life cycle. Standardizing the header will allow the information contained in the header to be leveraged programmatically for things such as auditing, project documentation, macro and dataset use tracking, consistency checking, and revision history reporting. The elements included in a header will vary from organization to organization but below is a discussion of some of the most common elements.
Required elements The following should be included in all program headers:
- Identification of the project of which the program is a part.
- Program name.
- Author identification which should be human readable and unique.
- Short description of program purpose.
- List of macros used in the program.
- Date program was first put into production, was finalized, or first passed validation.
- This date will be chosen based on the operational procedures used within the company /organization creating the program. The date should indicate the first date when the program was released for final use.
- Revision history.
- This is discussed further below.
Recommended elements The following are not required but are highly recommended in all program headers:
- The date on which program development started.
- All outputs generated by the program, including both file creation and modification.
- External files used such as datasets or databases that are used as data inputs to the program or macros used.
- Platform and operating system which the program was developed to run in.
- Software/programming language and version which the program was programmed in.
The revision history section is critical to document the revisions made to the program once it is put into production. A well designed revision history section should include the author of the change, date of release of the change, a short description of the change. Revision history may also include a version number for changes which can be used as a reference in the code.
Comments are important to help anyone reviewing, modifying or using a program to be able to quickly understand the code. All major data or proc steps should be commented, especially data specific and complex code. Ideally comments should be comprehensive, and should describe the rationale and not simply the action. For example, instead of simply typing "Access demography data", describe which data elements you are accessing and why they are needed, for example, “Bringing in DM to get gender and age and subset to include only the intent to treat population”. Comments can also include links to external documentation (requirement specifications, design documents. The programs can also be split up into sections by creating a different type of comments, e.g. many rows with asterisks. This helps to structure the program and make it easier for others to see an overview of the program.
All organizations should have standard naming conventions. Program naming conventions should make it possible to identify groups of related programs such as adverse events tables. Dataset and variable names should describe as best as possible their content. Temporary datasets and variables should be named consistently in a way that makes their purpose and role in the program clear. Where possible organisations should use industry level standards such as Clinical Data Interchange Standards Consortium (CDISC) standards for permanaent datasets and variables. This aids sharing of programs across companies and facilitates development of standard code. Space characters should be avoided in variable, dataset and output file names.
In order to be efficient and streamline the sharing of program code between programmers, with regulatory agencies, and with external partners or vendors, it is vital for code structure to follow standard conventions. SAS code which follows these conventions is much easier to read, modify, maintain, and correct. These conventions are divided into those which should be considered as required, and those which are merely recommendations to be followed as applicable.
- Do not overwrite existing datasets
- Each organization may have its own standards for using case within programming code but use of all uppercase should be avoided
- Separate data steps and procedures with at least one blank line
- Use ‘data=dataset’ option in procedure statements so that the dataset being used is explicitly stated to ensure that the statement will work if it is moved to another location
- End data steps and procedures with run or quit to provide a boundary and allow for independent execution
- Split data steps into logical parts
- Put each statement on a separate line
- Left justify global statements and data and procedure statements and their corresponding run and quit statements
- Indent statements belonging to a level by 2 to 5 columns (use the same number of spaces throughout the program), i.e. every nesting level should be visibly indented from the previous level.
- Do not use tabs for indentation because they will display differently depending on the platform and text editor being used, use blanks instead
- For do loops place the end statement in the same position as the do statement so that they can be easily matched
- Insert parentheses in meaningful places in order to clarify the sequence in which mathematical or logical operations are performed
- When converting character variables to numeric or vice versa, use the put and input functions to explicitly convert the variable to ensure that it is done in the way intended.
- Perform only one task per module or macro
- Use logical groupings to separate code into blocks
- Double space between sections
- Group similar statements together
- Define new variables with the attrib statement in order to ensure that the variable properties such as length, format, and label are correct instead of allowing them to be implicitly determined by the circumstances in which they are initialized in the code
Log File Checking
As part of development and validation practices, it is often mandated that the log file generated is checked to ensure that the program has executed in the correct intention. Many companies may have their own automatic log file checking utilities to aid in this, and there are many examples of such tools in widely available papers. “ERROR” and “WARNING” in logs should normally be avoided. There are sometime exceptions to this, such as warnings that are output from statistical models that do not have enough data. Ordinarily, any warnings that are deemed acceptable are to be documented. There are also some specific “NOTE”s that can indicate a problem. The common “NOTE”s that should normally be avoided include those relating to “repeats”, “more than one”, “uninitialized” and “referenced”.
Also, any user defined checks that have been added, such as from defensive programming, should be checked for in the log and followed up on. A company-specific naming convention for user defined checks can aid in this, so as the specific string can be searched for within the log. Examples of such conventions include “ISSUE:”, “USER:”, and “ALERT:”. Avoid the use of user-generated errors and warnings labeled "NOTE:", "WARNING:" or "ERROR:", as these may make it difficult to find genuine problems when searching the log.
Most organizations are now working across multiple platforms, commonly combining Windows and Unix environments. There can be many occasions where code will work on one platform and not on another. Portability is more than just working across multiplatform environments, it is also about making programs easier to be used across projects. Below are some suggestions to address some of the most common impediments to portability.
- Use rounding in newly created variables (if applicable) in order to avoid different results e.g. from 64 bit operating systems to 32 bit systems. (However give careful consideration to doing this and round at the limit of precision as otherwise it may affect results. Where rounding is only required for presenting results, do so after calculations and derivations are completed.)
- Avoid explicitly defining file paths in libname, filename, and %include statements requiring platform specific syntax such as forward slash or back slash.
- Avoid the use of X commands to execute statements directly on the operating system.
Hardcoding is the modification of the value of an item of source data within program code. Hardcoding should be avoided whenever possible in final code, and changes to source data should be done in data entry or capture systems which give better compliance to regulations such as FDA 21CFR11. Hardcoding may be done temporarily in order to get a program to run due to dirty data or correct for database inconsistencies. Permanent hardcoding to fix incorrect data values in a final database is strongly discouraged, but if it is unavoidable then it must be approved following a standard process (usually an SOP) and clearly documented using standard comments and PUT statements to the log to show what has been hard coded.
Defensive programming is an approach to programming intended to anticipate future changes of the data that might influence the coding algorithms. Ideally programs should be written in such a way that they will continue to work correctly in case of new or unexpected data values which did not exist at the time the code was developed. Analysis dataset and table programs are often developed in the early stages of a project or even when the only available data is test data. In these situations the data often does not contain all possible values of data points such as visits or time points, race values, and questionnaire responses, but the program must be able to handle those values when they do become present in the data at a later point.Current user rating: 99/100 (4 votes)