Top 40 Clinical SAS Interview Questions and Answers

The market for clinical trials is expanding, and as data becomes more crucial in decision-making, there is a growing need for SAS programmers. Sharpen your skills with our Clinical SAS interview questions and answers to ace the interviews with top companies. Explore our Clinical SAS course syllabus for your learning needs. 

Clinical SAS Interview Questions and Answers for Freshers

Here are 40 Clinical SAS Interview Questions to help with your preparation.

Interview Questions and Answers Fundamental SAS Concepts

1. Explain the difference between a DATA step and a PROC step.

The proc step and the data step are the two components that make up SAS applications. The data step supplies data into the SAS dataset, which is then examined in the proc phase. 

2. What are the various types of SAS libraries?

A collection of one or more SAS files that are kept and referred to collectively is called an SAS data library. The logical idea of an SAS data library is consistent across operating systems, and the files are members of the library.

WORK, USER, SASHELP, and SASUSER are the four unique SAS System libraries that offer convenience, assistance, and customization. 

SAS libraries are internet or disk drive folders that are specifically made for SAS use. 

3. In SAS, how do you deal with missing values?

In SAS, missing values can be handled in the following ways:

Use the UPDATEMODE= option: Whether missing values in a transaction data set replace pre existing values in a master data set is specified by this option. By default, MISSINGCHECK stops missing values from replacing values in a master data collection.

Use the MISSING function, which checks a character or numeric expression for a missing value before returning a numeric result. If an argument contains a missing value, SAS returns a value of 1. 

Use statistical functions: Missing values may be excluded from calculations using these functions, which include the SUM function and the SUM statement.

Use the MISSING option in the PROC statement or CLASS statement: By selecting this option, the analysis is guaranteed to incorporate observations for a class variable that have missing values. 

Recode any values that are missing: Code once more after setting values to missing.

Use new variable names: To prevent constructs that duplicate the variable name, use fresh variable names when generating or recoding variables.

4. What is the use of the SAS facility?

The SAS logging facility, SAS storage facility, and SAS applications facility are among the several matches for the SAS facility.

SAS logging facility: A framework for gathering, classifying, and filtering events to use in performance management, problem diagnostics, and other tasks. Database tables, client apps, and fixed files are just a few of the output devices to which the facility can write events. 

SAS storage: An enterprise-level storage solution for high-performance workstations, servers, and data centers. Performance, dependability, and multi-device support are hallmarks of SAS storage.

SAS Applications Facility (SAS/AF): The SAS apps Facility (SAS/AF) is a development environment with a point-and-click interface that enables users to create portable apps. 

5. Explain the purpose of SAS Array.

In SAS, arrays are used to store and retrieve a group of values based on an index value. They serve as a means of momentarily classifying and referring to variables that have similar traits, like being of the same kind. 

Here are a few applications for SAS arrays:

• Repeated calculations: By using arrays, fewer statements are required to carry out repetitive computations.  
• Creating variables: An existing collection of variables can be converted into new variables using arrays. 
• Restructuring data: Data can be restructured using arrays.
• Table lookups: Table lookups can be carried out using arrays.

6. Describe the concept of SAS functions with examples.

SAS functions are procedures that return a value after performing actions on parameters, such as variables, constants, or expressions. Numerical computations, data manipulation, and value creation are all possible with them.

Examples of SAS functions include the following:

• INT: It returns an argument’s integer part while truncating its decimal part. The integer value 2 will be returned by the INT function, for instance, if the argument is 2.5.
• MEAN: The arithmetic mean, or average, of parameters that are neither null nor missing is returned by MEAN.  
• FIND: It returns the location of a provided substring after searching a string for its first occurrence. FIND returns 0 if the substring cannot be located.
• ANYPUNCT: It returns the position of the punctuation character after searching a string for its first occurrence. ANYPUNCT returns 0 if no punctuation character is detected.

7. In SAS, how are datasets sorted and combined?

In SAS, the MERGE statement plus a BY statement inside a data step can be used to sort and combine datasets: 

• Sort the datasets: Sort each dataset according to the variable or variables specified in the BY statement before combining. 
• Use the MERGE statement: Utilize the MERGE command in the data phase to merge the datasets. After the MERGE keyword, list the datasets to be merged, with spaces between each entry.  
• Use the BY statement: To find the observations in the datasets that represent the same subject, utilize the BY statement in the data step. The BY variable or variables must share the same characteristics and be present in both datasets.  

8. What distinguishes an IF statement from a WHERE statement?

In data steps or procedures, 

• WHERE is used to subset observations according to a condition. It is employed to choose a subset of observations that satisfy a particular criterion from an SAS data set. 
• The IF statement, on the other hand, is used to conditionally run code in response to a condition.

9. In SAS, how can you make custom formats?

The SAS FORMAT method can be used to build custom formats in SAS by substituting formatted character values for raw data values:

• Give a reference to a library.
• To develop bespoke formats, use the Proc format.
• Start with the word value and list the custom formats you wish to generate.

For example, you can use the following code to build a custom numeric format called DEPTNO that associates department codes with their respective department names:

proc format; value deptno 10 = ‘Sales’ 20 = ‘Research’ 30 = ‘Accounting’ 40 = ‘Operations’; run;

10. What types of joins are there in SAS?

In SAS, the many kinds of joins are:

• Inner Join: It yields a result table with rows from one table that match rows from other tables. The two tables can be joined in either order using an inner join, which is a logical AND of the two tables.
• Cross Join: A Cartesian product of two tables is the outcome of a relational join. The product of all the observations in all the datasets being linked determines how many observations are in the output dataset.
• Full Outer Join: It retains both matching and non-matching rows from both tables by combining the left and right joins.
• Left Join: Using NULL values for non-matching rows, it retrieves every row from the left table and the matched rows from the right table. 
• Outer Join: In SAS, an outer join is a join operation that returns all rows from one or both tables that match the join criteria together with some or all of the unmatched rows. 
• Union Join: It is utilized to merge information from several tables. While UNION combines rows from different queries to create a single dataset, JOIN merges columns based on relevant data.
• Natural Join: Joining two or more tables based on shared columns is known as a “natural join.” The names and data types of the shared columns must match. The output is a cartesian product of the input tables if the tables being linked do not share a common column.

Explore more with our SAS course syllabus. 

Clinical SAS Specific Interview Questions and Answers

11. What is CDISC and why is it important in clinical trials?

A non-profit group called the Clinical Data Interchange Standards Consortium (CDISC) creates data standards for healthcare and medical research. Clinical trials benefit from CDISC standards because they: 

• Enhance data quality: CDISC standards aid in guaranteeing the accuracy and interpretability of clinical trial data.
• Facilitate the utilization of data: Regulatory reviewers can more easily comprehend and handle clinical trial data thanks to CDISC standards.
• Accelerate the procedure: Because CDISC standards speed up the regulatory process, they can assist cut down on the time and expense of drug development.
• Encourage transparency: From procedure to reporting, CDISC standards promote openness in the medical research process.  

12. Explain the SDTM and ADaM standards.

The Clinical Data Interchange Standards Consortium (CDISC) created the following standards for clinical trials: SDTM (Study Data Tabulation Model) and ADaM (Analysis Data Model):

• SDTM: Clinical trial data is gathered, arranged, and formatted using SDTM in order to be standardized and submitted to regulatory bodies.
  • The SDTM specifies a uniform format, variables, and documentation needs for clinical trial data.
• ADaM: It is used to transform SDTM data into datasets that are ready for analysis.
  • Tables, figures, and lists (TFLs) in research reports are created using ADaM data. 
  • Although it is not necessary to submit to regulatory bodies, ADaM is advised for use.  

13. How is data on adverse events handled in SAS?

When working with adverse event data in SAS, keep the following points in mind:

Reporting: It is important to notify the sponsor about adverse events as soon as possible, either in writing or verbally. Details and the event’s conclusion should be included in a comprehensive report that follows.

Analysis: The following should be taken into account while examining adverse events:

• The event’s character, severity, and result
• The clinical progression that preceded the incident
• The event’s timing in respect to the delivery of the study drug
• Relevant laboratory tests
• The research drug’s use in connection with the incident
• The intervention or therapy.

Grading: On a scale of 1 to 5, adverse occurrences are assigned a grade. In grade 0, there are no symptoms or issues; in grade 1, they are mild; and in grade 2, they are unpleasant but not harmful.

Importance: Data from adverse event reporting is crucial for resolving concerns about study participant safety.  

14. What are the common challenges in clinical data analysis?

Some of the common challenges in clinical data analysis include the following:

• Security issues especially to protect patient’s data.
• Inconsistent data quality arises from data entry errors.
• Regulatory compliances due to frequent requirements evolving.
• Interoperability issues due to integrating various data sources.
• Lack of skilled workforce and simplicity in financial resources.
• Standardization issues when adopting CDISC standards.
• Data management issues arise from under maintained sites.
• Managerial issues when accessing health care services.
• Real Time analytics issues when utilizing big data.
• Data structure issues due to low performed data.

15. How is safety analysis carried out in SAS?

SAS can help with safety analysis in a number of ways, including:

• Monitoring unsafe behavior: Geofencing and camera vision sensors are two tools that SAS can employ to monitor dangerous conduct.
• Issuing real-time alerts: Real-time alerting By altering dangerous behavior as soon as it occurs, SAS can send out alerts to stop accidents.
• Detecting and visualizing patterns: Finding and displaying trends SAS can examine past trends to enhance safety instructions, procedures, and training.
• Complying with PII regulations: SAS may assist in ensuring privacy compliance with secure settings and appropriate methods by adhering to PII standards. 

16. Describe the idea of derived variables and their creation process.

Derived variables are those that are produced by classifying or computing variables that already exist in a data set. Information that would not otherwise be accessible can be obtained through them. Derived variables are made in the following ways:

• Calculating a new variable: A numeric variable that displays the total of two or more additional numeric variables is one example. 
• Creating a categorical variable: For instance, you can use the continuous variable income to define income categories. 
• Re-grouping or re-classifying: To construct derived variables, you can reclassify or regroup the source variables. 
• Using published measures or scales: Derived variables can be constructed using established scales or measures. 

17. What is the role of SAS in clinical trial data management?

Clinical trial data management benefits greatly from the use of SAS (Statistical Analysis System), which helps in:

• Maintain and store data: SAS has the ability to maintain, exchange, and store clinical research data in a cloud-based repository. Both structured and unstructured data can be handled by it.
• Data analysis: SAS offers a variety of statistical techniques and algorithms for examining data from clinical trials. SAS, for instance, can be used by researchers to carry out descriptive statistics and regression analysis.
• Data visualization: To assist researchers in interpreting data and communicating their conclusions, SAS offers tools for producing tables, charts, and graphs.
• Monitor and report adverse events: In clinical trials, SAS assists researchers in monitoring and reporting adverse events.

18. How do you handle missing values in clinical data?

Applying machine learning to time series data requires careful consideration of how to handle missing values. 

• This is relevant to clinical data since the physiological profile of a patient is frequently not full at any given time. 
• The accuracy and execution duration of the imputation techniques are additional crucial factors to take into account. 

19. What are the common statistical procedures used in clinical trials?

The following statistical techniques are frequently employed in clinical trials:

• T-tests: Compare the two groups’ means.
• ANOVA: Examine three or more groups’ means.
• Chi-square tests: Examine the connections between the categorical variables.
• Logistic regression: Regression analysis using logistic regression Analyze binary results, such as treatment success or failure.
• Survival analysis: Examine time-to-event information, such as the amount of time until death or recuperation.
• Hypotheses testing: Verify the results of clinical trials against null hypotheses.
• Regression analysis: Examine the relationship between one or more independent variables and a dependent variable.  

20. How do you create analysis datasets from raw data?

There are 6 steps are involved in creating analysis datasets: 

Step 1: Clean up the data: Data wrangling, also known as data cleaning, is the act of identifying and fixing duplicate or erroneous records in your collection. 

You will prepare the raw data for analysis by transforming it into a more useable format throughout the data wrangling process.

Step 2: Identify the Right Questions: Decide the key questions you want to address with your analysis. 

• These inquiries must be directly tied to a particular business issue and readily quantifiable. 
• Ask the business team requesting the analysis to include specific information about their goals, expectations, and plans for using the data. 
• Their suggestions can help you prioritize the questions in your analysis.

Step 3: Break Down the Data Into Segments: Dividing your dataset into more manageable, well-defined groupings is frequently beneficial. In addition to making your study easier to handle, segmenting your data will help you stay on course.

Step 4: Visualize the data: Data visualization, or the process of producing graphical representations of data, is one of the most crucial aspects of data analysis. 

You can quickly spot any trends, patterns, and glaring outliers by visualizing the data.

By producing captivating images that accurately depict the data, you can also successfully convey your results to important stakeholders, who can promptly infer conclusions from the visualizations. 

Step 5: Use the Data to Answer The Questions: Review the questions you posed at the start of the data analysis process once your data has been cleaned, arranged, transformed, and visualized. Analyze your findings and assess whether the information aids in the resolution of your initial queries. 

Step 6: Supplement with Qualitative Data: Lastly, as your research draws to a close, keep in mind that this dataset is just one component.

It’s crucial to combine your quantitative results with qualitative data, which you might get through surveys, interviews, or testimonies. Although the dataset can tell you what is happening, qualitative information may frequently assist you understand why.

Learn Clinical SAS online course program for further understanding. 

Clinical SAS Interview Questions for Experienced

Here are the interview questions on various clinical SAS in advanced level.

21. Explain the concept of macro variables in SAS and how they are used.

Macro variables in SAS are string variables that hold values that can be used to change program text. Another name for them is symbolic variables.

The following information relates to macro variables in SAS:

• Meaning: A name-value pair defines a macro variable, with the value being the text that will be used in place of the name when the macro variable is called.
• Creation: The %let statement, Proc sql, and other macro language capabilities can be used to create macro variables.
• Scope: Both local and global macro variables are possible. Only SAS programs defined as part of the program can access local variables.
• Storage: A macro symbol table in memory contains macro variables. Up to 64k bytes of data can be stored in them. 
• Use: Character strings can be stored and altered using macro variables, and data can be retrieved from a data step and stored in a macro variable. 
• Printing: A list of macro variables in the SAS log can be printed using the %PUT command. 
• Special character: Macro quoting can be used to safeguard special characters, which are any characters that do not appear in a legitimate SAS name. 

22. What is the difference between a %LET and %DO loop?

A %LET loop uses a placeholder value inside a loop, whereas a %DO loop repeats a series of instructions until a condition is met:  

%DO loop: It is used until a condition is satisfied by repeating a series of statements. 

• In Visual Basic, sentences can be repeated an infinite number of times using a Do…Loop statement. 
• In SAS, a Do Until loop continues until a logical assertion is true, whereas a Do While loop continues while a logical expression is true.  

%LET loop: A loop’s use of a placeholder value. A let variable in JavaScript, for instance, is only accessible within the block in which it was declared.  

23. How do you write efficient SAS code?

Efficiency will be simply defined as using less computer resources to get more results for the purposes of this discussion. When an SAS program is submitted, the computer needs to:

• Install the necessary software in memory.
• Build the program
• Locate the data it will run on
• Carry out the actions it requests
• Report the outcomes for your review.

Time and space are necessary for all of these jobs. CPU time, I/O time, and memory make up a computer program’s time and space.

• CPU time: It is how long it takes the Central Processing Unit to complete the tasks you give it.
• I/O time: It is the amount of time a computer spends on input and output. Reading data from a storage device is referred to as input. Writing to storage or a display device is referred to as output.
• Memory: the amount of workspace that the CPU needs to dedicate to the program’s operations.

24. What are the performance implications of different SAS procedures?

The following are examples of how various SAS methods may affect performance:

High-performance procedures: These processes can benefit from distributed multiple-machine or multithreaded single-machine parallel processing. 

• They can be applied to dimension reduction, model selection, predictive modeling, and the identification of critical variables.

In-database processing: It is advised to use in-database processing rather than downloading distant data onto the SAS computer when SAS communicates with an external data store.

• When working with Hadoop, where data can be in the terabyte or petabyte range, this is very crucial.  

IF-THEN/ELSE statements: Performance can be significantly impacted by the ELSE clause. 

• The second IF statement may be executed needlessly and take longer to execute if the ELSE clause is left out. 

Buffer size: In general, the best SAS performance can be obtained by using a buffer size that is a multiple of the disk stripe size. 

Data compression: Data compression can reduce I/O traffic and conserve disk space. 

CPU: A faster CPU can have an impact. A CPU should be shared by fewer users for statistically demanding tasks. 

25. How do you debug SAS programs?

You can use either the SAS Code Debugger or the DATA Step Debugger to debug SAS programs:

DATA Step Debugger

The DATA Step Debugger can be used as follows:

• Click the bug icon in the program editor window to activate the debugger.
• Click the bug icon to start the debugger.
• Go line by line through the program.
• Enter EXAMINE _ALL_ on the command line to examine the values of the variables.

SAS Code Debugger

The SAS Code Debugger can be used as follows:

• Open a web browser, navigate to the SAS Code Debugger URL, and log in.
• To debug, start the code using the OPTIONS statement.
• Run the code within an SAS program.  

26. What is the use of the CALL SYMPUTX macro function?

Dataset values are stored in macro variables by SAS’s CALL SYMPUTX macro function:

• Saves time: In complex applications, using CALL SYMPUTX can save time and lower the likelihood of errors. 
• Eliminates the trailing and leading blanks: CALL SYMPUTX eliminates leading and trailing blanks from the value when it is run on the Compute Server.
• Stores in a table of macro symbols: A macro variable that is kept in a macro symbol table is given the value by CALL SYMPUTX.
• Specifies symbol table: You can designate the symbol table where the macro variable should be stored by using CALL SYMPUTX. 
• Uses most local symbol table: The version in the most local symbol table is used by CALL SYMPUTX if the macro variable is present in any symbol tables.  
• Stores in the same symbol table as CALL SYMPUT: The macro variable is stored in the same symbol table as the CALL SYMPUT procedure if symbol-table is left empty or omitted. 

27. How are custom macros made in Clinical SAS?

The following procedures can be used to develop a custom macro in SAS:

• To construct the macro, add a line above the proc print.
• To begin the syntax, use the term “macro” and a percent sign.
• Give the macro a name.
• To incorporate parameters, include them in open and closed parenthesis.
• Put the “men’s” sentence at the end of the macro.

28. What are the various types of SAS logs?

Everything that occurs during an SAS session or program is documented in the SAS log, including the statements that are executed, the duration of the steps, and whether any errors occurred. The operating environment and SAS execution method determine the contents and destination of the SAS log.

Here are a few methods for setting up an SAS log:

LOG= system option: The SAS log’s name

LOGPARM= system option: It enables you to create a new SAS log, decide when to write to an existing one, and append or replace an existing one.

Original program statements with line numbers, SAS messages, the names of generated data sets, and the quantity of variables and observations in generated data sets are all included in the SAS log.  

29. How are statistical reports created using SAS?

To generate statistical reports using SAS, you can:

• Describe the columns: To specify the report page’s columns and column widths, utilize the COLSET macro.
• Transfer data and text: To transfer data and text values to the designated columns and rows, use the macros MOVE and NMOVE.
• Compute the summary statistics: Invoke a macro that performs a process, generates statistics for an SAS data set, and then handles data reformatting.
• Apply SAS protocols: The REPORT and TABULATE procedures allow for some customisation, although the majority of SAS processes provide output in a standard style.
• Utilize ODS features and DATA steps: These offer the greatest degree of customization and output selection options.
• Follow the format procedure: Make formats that are user-defined and apply them to report variables.
• Make use of the by and where statements: Group and subset a report.
• Make use of ods statements: Output directly to several ods destinations.
• Give a definition of style: In the ods destination declaration, use the style= option.
• Make use of the COLUMN declaration: Indicate the variables to include in the report and how the columns should be arranged.  

30. What part does SAS play in regulatory submissions?

What part does SAS play in regulatory submissions?

SAS assists in producing documentation that adhere to regulatory standards, which is crucial for clinical trial regulatory submissions:

• Data management: Clinical trial data can be effectively cleaned, transformed, standardized, and handled by SAS. Additionally, it supports a wide range of data types and can integrate data from multiple sources.
• Data quality and validation: SAS provides strong validation processes to guarantee data quality and integrity. It can assist in locating and correcting data inaccuracies or discrepancies.
• Regulatory submission-ready documents: SAS offers the resources needed to produce documents that are prepared for submission to regulatory bodies.
• Custom reporting: The results of the clinical study can be presented in bespoke reports created with SAS. Stakeholders and regulatory agencies can use these reports to inform their decisions.
• Data analysis: Tables, lists, figures, and graphs can all be created and maintained with SAS.
• Statistical analysis: Programmers can perform intricate statistical analysis using SAS to look for patterns, deviations, and noteworthy impacts in clinical data.
• Sample size determination: Determining the right sample size for clinical trials can be aided by SAS analysis.

Most regulatory bodies worldwide use SAS as the norm for clinical programming and data analysis. 

Enroll in our SAS online training at SLA.

Clinical SAS Interview Questions on Data Management and Programming

31. How do you validate clinical data in SAS?

In SAS, you can validate clinical data by:

• Establish integrity restrictions: Establish integrity limitations using SAS methods.
• Impute values that are missing: Impute missing values in data using the LAG function.
• Locate any missing values: To locate missing values in CASL, utilize the MISSING function.
• Create an audit file first: To start an audit file and update the data set, use the DATASETS method.

A key component of clinical data management (CDM) is clinical data validation. It guarantees the accuracy, completeness, and consistency of clinical trial data. Errors, inconsistencies, or contradictions are found and addressed by a methodical analysis of the data.  

32. What are the common data quality checks performed in clinical trials?

In trials, the following data quality checks are frequently carried out:

What are the common data quality checks performed in clinical trials?

Here are some common data quality checks performed in clinical trials:

• Quality control and quality assurance: To uphold and enhance data validation standards, these procedures include defined protocols, frequent audits, and continuous improvement techniques. 
• Data quality assurance: This procedure finds and fixes mistakes, inconsistencies, and inaccuracies in data from clinical trials. 
• Regulatory compliance: This procedure makes sure that the data management procedures follow the law and moral principles. 
• Data collection: Standardized forms or questionnaires are used in this process to gather information from participants. 
• Data validation: This procedure entails system locks, data checks, implementation, and planning.
• Database locking: It is a procedure that keeps the database clean and restricts data modifications.  

33. How do you handle data inconsistencies and errors?

Here are several strategies for dealing with errors and inconsistencies in data:

• Analyze data: Check the information against a reliable reference or the original source. To look for duplicates, erroneous numbers, and outliers, use programs like R, Python, or Excel.
• Identifying missing values: Select a method for dealing with missing values, such as removal or imputation.
• Handle outliers: To deal with outliers, apply strategies like transformation or winsorization.
• Data standardization: To deal with scale disparities, standardize or normalize the data.
• Apply encoding techniques: For categorical variables, apply encoding techniques.
• Employ error handling mechanisms: To find, record, and fix mistakes, make use of data integration technologies’ strong error handling mechanisms.
• Document data cleaning process: Note the procedures, techniques, equipment, and justification for your data cleaning efforts.
• Perform routine security audits: These can assist in locating weaknesses.
• Create a data governance framework: To preserve data quality and guard against abuse, use data governance.
• Perform routine cleaning and reviews: Databases should be reviewed and cleaned on a regular basis to preserve data quality.
• Remove duplicates: Eliminate redundant data since it may induce bias and compromise the analysis’s accuracy.  

34. What part does SAS play in imputation and data cleaning?

SAS is a software and computer language that can assist with data imputation and cleaning in a variety of methods, such as:

• Data cleaning: SAS can assist in confirming that data adheres to criteria, including those pertaining to categorical variable categories and numerical value ranges.
  • Additionally, SAS can assist in locating missing data and eliminating rows that include them.
• Imputation: Using a range of methods, from straightforward built-in activities to more complex ones that can call for some specially written SAS code, Imputation SAS can assist in imputation missing values.
• Data Quality: SAS can use technologies like fuzzy matching, parsing, casing, and standardizing to assist increase the consistency and integrity of data.
• Data Preparation: Prebuilt transformations and data purification features in SAS can make data preparation easier.
• Data Collaboration: Collaboration and reuse of data preparation tasks can be facilitated by SAS, guaranteeing quality and consistency across the data life cycle.

SAS offers a visual interface that can facilitate data preparation without requiring SQL, coding, or specialist knowledge.  

35. In SAS, how can you make custom tables and listings?

In SAS, you may make custom tables and listings by:

• Design the table: Specify the size of the table, the variables used for categorization and analysis, and the desired statistics.
• Generate SAS code: To generate tabular reports, utilize the PROC TABULATE statement.
• Customize the table: Give the table a label, format, style, font, and weight.
• Create a customized table template: To make a template that can be kept in a template store, follow the ODSTABLE process.
• Create a table without rows: To specify the columns and their properties, use the CREATE TABLE statement.
• Add data items: Fill up each data item’s Name, Label, and Description fields.
• Specify the type option: Choose the type choice according to the column’s data type.
• Enable the Identity option: If there are identity values in the column, enable this option.
• Turn on the option to use the table’s key column: To indicate that a column offers a distinct value for the data set, enable this option for a data item.

A listing report shows one line for each observation in the table, whereas a table is a collection of data arranged in rows and columns. 

36. How are statistical analysis plans created using SAS?

A software platform called SAS (Statistical Analysis System) can be used to conduct statistical analysis and create statistical analysis strategies.

• Data analysis: Statistical analysis, predictive modeling, and data mining are all possible using SAS.
  • It offers an interface and tools for processing complicated data, displaying information, and carrying out sophisticated analyses.
• Statistical analysis plans: Plans for statistical analysis can be created using SAS. 

The following advice can help you write a plan for statistical analysis:

• Add a title and identifying details, including the protocol number, version number, date, and research title.
• Provide an introduction and a summary of the study, including the study design and background data.
• Add goals and theories, including exploratory, secondary, and primary goals and theories.

Healthcare, banking, and retail are just a few of the industries that employ SAS, a popular analytics program. It was created at North Carolina State University at the beginning of the 1970s.

Enhance your skills with our data analytics course syllabus. 

Clinical SAS Programmer Interview Questions for Experienced

37. Talk about various imputation methods and the situations in which they are acceptable.

Here are the methods: 

Find Missing Values: To find missing values, use SAS functions such as missing() or nmiss(). 

Imputation Techniques:

• Mean/Median Imputation: Use the variable’s mean or median to fill in the missing values. 
• Last Observation Carried Forward (LOCF): Use the most recent observed value to fill in the missing values.
• Next Observation Carried Backward (NOCB): Use the subsequent observed value to fill in the missing values.
• Multiple Imputation: Use statistical models to impute missing values more than once to produce several complete datasets.

Suitable Use Cases:

• Mean/Median Imputation: For continuous data with a missing at random (MAR) mechanism, mean/median imputation.
• LOCF/NOCB: For longitudinal data, particularly in clinical studies, LOCF/NOCB is frequently utilized.
• Multiple Imputation: Offers more precise statistical inference and manages intricate missing data patterns. 

38. Describe how ADaM and SDTM are used in clinical data analysis. How do you make sure your SAS programs adhere to CDISC standards?

• SDTM: Clinical data is arranged into standardized tables and listings using the SDTM (Study Data Tabulation Model).
• ADaM (Analysis Data Model): Converts SDTM data into datasets that are prepared for analysis.

Ensuring Compliance:

• Validate Data: To find mistakes and compare data to CDISC criteria, use SAS.
• Comply with Naming Conventions: Give variables and datasets the names specified by CDISC.
• Make use of CDISC-compliant Macros: Automate data transformations by utilizing CDISC-compliant macros.
• Record Data Mappings: Clearly record how raw data and SDTM/ADaM datasets are mapped.

39. How would you improve the performance of SAS code, particularly when working with big datasets?

• Array Processing: To cut down on execution time, use arrays to process several variables in a single loop.
• Subsetting: To filter data and minimize the quantity of data processed, use the WHERE and IF statements.
• Effective Data Access: To access data effectively, use the SET statement with the POINT= option.
• Steer clear of pointless calculations: Reduce the number of calculations that are done twice.
• For complex queries, use PROC SQL: Make use of SQL’s capabilities for intricate data processing.
• Think About Parallel Processing: For huge datasets, make use of SAS’s parallel processing capabilities. 

40. In SAS, how would you conduct a survival analysis? Describe the main steps that are involved.

Data Preparation: 

• Determine the censoring indicator and time-to-event variable.
• Verify that the time-to-event variable is used to sort the data.

Model Fitting: 

Fit survival models (such as Cox regression and Kaplan-Meier) using PROC LIFETEST.

Visualization: 

Use PROC LIFETEST or PROC SGPLOT to create survival curves and log-rank tests.

Interpretation: 

To make inferences, analyze p-values, confidence intervals, and hazard ratios. 

Conclusion

We covered some of the most crucial elements of Clinical SAS programming questions in this article. It is essential to have a solid foundation in SAS programming, comprehend clinical data management principles, and be able to apply your knowledge to practical situations. Enroll in our Clinical SAS training in Chennai if you want to ace a clinical SAS interview.