A Complete Guide to Clinical SAS Programming

This article explains the basic concepts and methodologies used in the Clinical SAS domain and how they are important for pharmaceutical and biotechnology companies. It covers all of the essential topics that you need to know to have a career in the clinical SAS domain.

Clinical SAS Programming
Table of Contents

What is Clinical SAS Programming?

Clinical SAS programming is the use of the SAS programming language to manage, analyze, and report clinical trial data. SAS is a statistical software package that is widely used in the pharmaceutical and biotechnology companies for a variety of tasks, including data management, analysis, and reporting.

SAS in Clinical Domain

SAS is used in the clinical domain for the following tasks:

  1. Data Management: SAS is used to clean, transform, and manage clinical trial data. It can handle large datasets and perform data validation, data integration from different sources.
  2. Statistical Analysis: SAS is used for statistical analyses, such as descriptive statistics, inferential statistics, regression analysis, survival analysis, and analysis of variance (ANOVA). These analyses help researchers and clinicians to draw conclusions from clinical trial data.
  3. Data Validation: Checking the accuracy, consistency, and completeness of clinical trial data by performing data validation checks, identifying discrepancies, and resolving data-related issues.
  4. Safety Reporting: Generating safety reports and listings to monitor adverse events and safety data during the trial.
  5. SDTM (Standard Data Tabulation Model) Conversion: Converting clinical trial data into SDTM format, which is a standardized data model for regulatory submission.
  6. ADaM (Analysis Data Model) Implementation: Creating ADaM datasets, which are analysis-ready datasets used for statistical analysis.
  7. Report Generation: Preparing clinical trial reports, including integrated summaries of safety and efficacy (ISS/ISE), clinical study reports (CSRs), and other regulatory documents.
  8. Data Quality Control: Implementing quality control procedures to ensure the accuracy and reliability of analysis results.
  9. Data Visualization: Creating data visualizations, such as graphs and plots, useful for data exploration and presentation of results.
  10. Automation: Creating SAS macros to streamline and automate repetitive tasks and improve programming efficiency.
  11. Medical Coding: Performing medical coding of adverse events, concomitant medications, and medical history terms using standardized dictionaries like MedDRA (Medical Dictionary for Regulatory Activities) and WHO Drug.

Career in Clinical SAS

There are several job roles within the Clinical SAS domain, including Clinical SAS Programmer, Clinical Statistical Programmer, Biostatistician, and Clinical Data Manager. Each role has its specific responsibilities in data management, analysis, and reporting.

Skills for a Clinical SAS Programmer

Here are some of the skills required for a Clinical SAS Programmer.

  1. Educational Qualifications:
    • Bachelor's degree in a relevant field such as Computer Science, Statistics, Life Sciences, Mathematics, or a related discipline.
  2. SAS Programming Skills:
    • Proficiency in SAS programming is the core requirement for this role. Knowledge of SAS Base programming is essential, and familiarity with SAS Macro language is often preferred.
    • Ability to write, debug, and optimize SAS code for data manipulation, analysis, and reporting.
  3. Clinical Research Knowledge:
    • Understanding of clinical research processes, including clinical trial phases, data collection, and regulatory guidelines.
    • Knowledge of CDISC (Clinical Data Interchange Standards Consortium) standards, such as SDTM and ADaM, which are commonly used in clinical trials.
  4. Data Management and Analysis:
    • Experience in data management and data analysis methodologies.
    • Ability to clean, validate, and organize clinical trial data for analysis.
  5. Statistical Analysis:
    • Knowledge of statistical concepts and methodologies used in clinical data analysis.
    • Ability to interpret statistical results and apply appropriate statistical tests.

Skills for a Biostatistician

Here is a list of the skills required for a Biostatistician.

  • Educational Qualifications: Master's or Ph.D. in Statistics, Life Sciences, Mathematics, or a related discipline.
  • Statistical Knowledge: A deep understanding of statistical concepts and techniques is the foundation of a BioStatistician's role. This includes proficiency in hypothesis testing, regression analysis, survival analysis, experimental design, and multivariate analysis.
  • Understanding of Clinical Trials: Knowledge of clinical trial methodologies and regulatory requirements is vital, especially for BioStatisticians working in clinical research and drug development.
  • Statistical Software: Proficiency in statistical software packages such as SAS, R or Python for conducting statistical analysis and generating insights.
  • Experimental Design: Knowledge of experimental design principles is important for planning and executing studies in a way that allows for robust statistical analysis.
  • Data Visualization: The ability to create meaningful and informative data visualizations helps in presenting findings and insights to non-statistical audiences.

Benefits of Learning Clinical SAS Programming

Here are some of the benefits of learning clinical SAS:

  • High demand: There is a high demand for clinical SAS programmers in the pharmaceutical, biotechnology and clinical research industries.
  • Entry into Clinical Research Field: Clinical SAS programming is a gateway to enter the clinical research field. It allows you to work on cutting-edge research projects.
  • Industry-Relevant Skill: SAS is widely used in the life sciences industry, making it a highly relevant skill for individuals interested in working in the field of healthcare and pharmaceuticals.
  • Global Demand: SAS is used globally in clinical research, creating opportunities for individuals to work internationally and collaborate on global projects.

Clinical Trial

A clinical trial is a type of scientific study where researchers test the effectiveness and safety of different medical treatments or interventions on volunteers. These interventions can be anything from medicines, vaccines, medical devices, to screening methods. Some interventions are aimed at diagnosing diseases, others may prevent illnesses, and some are treatments to help people get better.

Phases of Clinical Trials

Imagine a group of scientists has developed a potential new medicine to treat the flu. They want to find out if it's safe and effective in helping people recover from the flu faster.

Phase 0 (Microdosing Phase):
  • Number of Volunteers: Usually involves 10-15.
  • Duration: Typically lasts for a short period, usually a few days to a week.
  • Objective: Phase 0 is an exploratory phase that uses subtherapeutic doses of the experimental treatment. The main objective is to obtain early pharmacokinetic (how the body processes the drug) and pharmacodynamic (how the drug affects the body) data. This phase helps researchers understand how the drug behaves in the body and how it's metabolized before moving on to larger Phase 1 trials.
Phase 1 (Human Pharmacology Trials):
  • Number of Volunteers: Usually involves a small number of healthy volunteers (20-100).
  • Duration: Typically lasts 6 to 12 months.
  • Objective: Safety and dosage. The main objective of Phase 1 is to assess the safety of the new treatment or intervention. Researchers want to understand how the treatment is processed by the body (pharmacokinetic), how it behaves at different doses (pharmacodynamic), and whether there are any harmful side effects. Efficacy is not the primary focus of this phase.
  • % Continuation: Around 70% of the drugs move to the next phase.
Phase 2 (Therapeutic and Exploratory Trials):
  • Number of Volunteers: Enrolls a larger group of patients (100-300).
  • Duration: Can last from 6 months to several years.
  • Objective: Efficacy and side effects. In Phase 2, the main focus is to evaluate the treatment's effectiveness and further assess its safety. Researchers aim to gather more data on how well the treatment works in treating the target condition or disease and identify the optimal dosage range.
  • % Continuation: Around 33% of the drugs move to the next phase.
Phase 3 (Therapeutic Confirmatory Trials):
  • Number of Volunteers: Involves an even larger group of patients, often thousands (1000-3000).
  • Duration: Can last from one to four years, depending on the complexity of the trial and the number of participants.
  • Objective: Efficacy and monitoring of adverse reactions. The main objective of Phase 3 trials are to confirm the effectiveness of treatment in a larger and more diverse (heterogeneous) population. Researchers gather more data on safety and efficacy to establish the treatment's benefits and monitor for any adverse reactions in a larger and more diverse population.
  • % Continuation: Around 25-30% of the drugs move to the next phase.
Phase 4 (Post-Marketing Surveillance):
  • Number of Volunteers: Larger participants, as the treatment is now available to the general public.
  • Duration: Several years.
  • Objective: Long-term safety and efficacy. The main objective of Phase 4 is to continue monitoring the treatment's safety and efficacy in real-world settings. Researchers collect data on its long-term effects, potential rare side effects, and interactions with other medications.

Clinical Trial Study Design

Clinical trial study design determines how the trial will be conducted, what data will be collected, and how the results will be analyzed and interpreted. It is to investigate the safety, efficacy, and effectiveness of a medical intervention or treatment. There are several common types of clinical trial study designs, including:

  • Randomized Controlled Trial (RCT): Participants are randomly assigned to different groups for comparison of treatment effectiveness.
  • Double-Blind Trial: Participants and researchers are unaware of who receives the treatment or control to reduce bias.
  • Crossover Trial: Participants receive multiple treatments in randomized order, useful for chronic conditions.
  • Parallel Group Trial: Participants are divided into different groups, each receiving a different treatment throughout the study.
  • Non-Randomized Trial: Participants are assigned to treatment groups based on specific criteria when randomization is not possible or ethical.
  • Single-Arm Trial: All participants receive the same treatment without a control group, often used in early-phase safety assessments.

CDISC Standards

CDISC, a worldwide non-profit organization, is responsible for creating data standards in the pharmaceutical industry. There are three distinct standard data models developed by CDISC specifically for regulatory submissions.

  • Study Data Tabulation Model (SDTM): Standard structure for clinical trial data sent to regulatory authorities like FDA in data submission package. It is the raw data for regulatory submission.
  • Analysis Data Model (ADaM): Uses SDTM domains to develop data sets for summarizing and analyzing clinical data. ADaM data sets support the trial analysis.
  • Define-XML: Provides machine-readable version of SDTM and ADaM data set specifications and complex data derivations. It helps FDA work efficiently with data submission.

What is SDTM?

SDTM (Study Data Tabulation Model) is a standard for pharmaceutical companies to submit data to FDA (Food and Drug Administration). In other words, SDTM is a widely accepted standard used to structure and organize data from clinical trials. It ensures that the data is presented in a consistent format when submitted to regulatory agencies, making data sharing and comparisons easier. SDTM standardizes variables like demographics, adverse events, and medical history.

What is ADaM?

In clinical programming, ADaM stands for Analysis Data Model. It is an industry standard designed to structure data specifically for statistical analysis and reporting purposes.

Difference between STDM and ADaM

  • Data: SDTM standardizes variables such as demographics, adverse events, and medical history, whereas ADaM standardizes analysis datasets such as efficacy, safety, and trial design.
  • Purpose: SDTM is primarily focused on organizing and standardizing data collected during clinical trials for regulatory submissions, whereas ADaM is designed to structure data specifically for statistical analysis and reporting purposes.

Important Documents for SDTM and ADaM

Below is a list of some important documents required for creating SDTM and ADaM.

  • Protocol: This is a detailed summary and guide for the study. It includes information about how the study is designed, when assessments will take place, and the methods used for analysis. Before anything else, this Protocol needs to be reviewed and approved by Institutional Review Boards (IRBs), regulatory authorities, and the study sites.
  • Blank Case Report Form (CRF): The Blank CRF is a form used to collect information from each patient participating in the study. The data manager creates this form, and then it's checked by statistical programmers, biostatisticians, and other relevant team members to ensure all necessary data for analysis is being captured. Finalizing the CRF can only happen after the Protocol is fully established.
  • Statistical Analysis Plan (SAP): The SAP is a plan created by the study's biostatistician. It outlines how the study data will be analyzed and interpreted.
  • Table, Figure, and Listing templates (TFLs): These templates are designed by the study's biostatistician to provide detailed content for statistical programmers. The programmers will use these templates to create actual tables, figures, and listings once the SAP is stable.
  • SDTM Annotated Case Report Form (SDTM aCRF): The SDTM aCRF is a version of the Case Report Form that has been annotated by the statistical programmer. It helps the programmer understand and create the structure of SDTM domains.
  • SDTM Specifications: These specifications contain details on how to generate the SDTM domains. They cover important information like how to program all the domains, the lengths of variables, labels, formats, and instructions on how to create each variable. These specifications are developed by the statistical programmer in conjunction with the SDTM aCRF, as both documents are closely related and depend on each other.
  • ADaM Specifications: These specifications provide information about the analysis data sets from SDTM domains, as well as any new variables and derivations needed for the analysis in ADaM data sets. The statistical programmer creates these specifications, but they can only be generated once the SAP and TFL shells are stable.
  • Define-XML: This is a machine-readable version of specifications, which includes both the SDTM Define-XML document and the ADaM Define-XML document. It provides more detailed information about how the data was created and structured.

Process Workflow

The work process starts with the Case Report Form (CRF), which is used to collect raw data from clinical trials conducted at various sites worldwide.

Once the CRF is ready and data is gathered, the clinical statistical programmer uses this data to create standardized groups of information called Study Data Tabulation Model (SDTM) domains. These domains organize the data in a consistent manner.

Later, the clinical statistical programmer creates Analysis Data Model (ADaM) data sets from the SDTM domains to support the analysis of the clinical trial data. Then, the clinical statistical programmer generates the Tables, Figures, and Listings (TFLs) that need to be included in the clinical study report submitted to regulatory authorities for assessing the safety and efficacy of the study drug.

Clinical SAS Programmer Process Workflow

Steps to Generate SDTM Datasets

Following are the steps to generate SDTM datasets from raw data.

  • Define Variables and Domains: Begin by reviewing the SDTM documentation to identify the necessary variables and domains based on the data collected during the clinical trial. Common domains typically include Demographics (DM), Adverse Events (AE), Exposure (EX), Disposition (DS).
  • Transform Raw Data: Use of SAS programming techniques to convert the raw data into SDTM datasets. This process will involve tasks such as data cleaning, variable mapping, and implementing SDTM-specific rules and formats.
  • Apply SDTM Guidelines: Ensure adherence to CDISC (Clinical Data Interchange Standards Consortium) guidelines and specifications for each domain. Properly assign variables to their respective datasets and domains following the CDISC standards.
  • Perform SDTM Validation: Validate the generated SDTM datasets by running comprehensive checks. This includes comparing the transformed data against SDTM guidelines and executing consistency checks to ensure the accuracy and completeness of the datasets.
  • Generate Define.XML and Documentation: Produce Define.XML files and accompanying documentation. These files provide essential metadata and detailed descriptions of the SDTM datasets, which are crucial for regulatory submissions and data interpretation.

Steps to Perform Survival Analysis

Survival analysis is the statistical technique commonly applied in the clinical domain. It analyzes the time it takes for an event of interest to occur such as time to death, time to disease recurrence, or time to a specific medical event. Following are the steps involved in performing survival analysis.

  1. Import Data: Load your data into SAS using PROC IMPORT. The data should include information on the event of interest (e.g., start and end time, event status) and any relevant covariates (e.g., age, gender, treatment).
  2. Data Preparation: It includes data cleaning, handling missing values, transforming variables, and creating new variables if needed.
  3. Define the Event: Determine the event of interest for your survival analysis. The event could be anything like death, failure, relapse, etc., depending on the context of your study.
  4. Descriptive Analysis: Generate summary statistics and Kaplan-Meier survival curves to understand the overall survival experience of your sample.
  5. Survival Model Selection: Choose the appropriate survival model for your analysis. It includes the Kaplan-Meier estimator, Cox proportional hazards model, or parametric survival models.
  6. Conduct the Analysis: Carry out the analysis using SAS procedures like PROC LIFETEST for non-parametric analysis, PROC PHREG for Cox proportional hazards models, or other specialized procedures for specific survival models. Specify the variables of interest, such as treatment group, covariates, or time-dependent factors.
  7. Interpret the Results: Analyze the output generated by SAS procedures to interpret the survival analysis results. Understand the hazard ratios, survival curves, confidence intervals, and p-values to draw conclusions about the effects of variables on survival.
  8. Report and Visualize Results: Present your results in a clear and concise manner, including appropriate tables, graphs, and figures to convey the key findings of your analysis.
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