Hack into SAS Clinical Trials Programming Certification

Discover the SAS clinical trials programming certification exam formats, including accelerated and normal options, registration steps, pricing, and key content areas such as data management, analysis, macro programming, and reporting.

Explore a road map for transforming SAS data into analysis-ready clinical trial data, guiding SAS programmers to transition from general programming to clinical statistical programming, applying standards for pharmaceutical analytics.

Clinical statistical programmers transform raw clinical data into standard structures for tabulations and analysis, generate sap-based outputs, export data via platform-independent transport formats, and discuss observational versus interventional trials.

Explore the drug and device development process, from in vitro screening and animal preclinical studies to phase i–iii clinical trials, FDA submission, and post marketing safety.

Understand how randomization minimizes bias by creating comparable treatment and placebo control groups, and explore blinding schemes from double blind to triple blind in clinical trials.

Classify clinical data into SDTM domains such as D.M. and S.V., L.B. and Vess, using supplemental qualifiers when needed and RelREC to describe cross-domain relationships.

Explore SAS V5 transport XPT files, a platform-independent format not limited to SAS and usable on any platform, with variable names limited to eight characters and labels up to forty.

Explore detecting missing values and outliers with SAS proc freq and univariate, and interpret descriptive statistics, p-values, and graphical outputs for both categorical and continuous data.

Explore how to access SAS metadata using the dictionary with proc sql, describing dictionary options and querying dictionary columns to reveal library contents and variable metadata.

Apply data set options and labels in SAS data steps to control variable presence with drop and keep, and distinguish data set labels from variable labels.

Transform clinical trial data using SAS with transpose and retain, apply baseline and carryforward concepts, convert data types with input and put, and count events.

Compare proc transpose and a data step approach to transpose clinical data, handling missing values and converting between long-to-wide and wide-to-long formats, while avoiding warnings and errors in clinical programming.

Learn to categorize continuous variables with mutually exclusive ranges using if-then-else, and transform SAS dates with numeric and character functions to extract year, month, and day.

Learn how to retain baseline for post-baseline data, define baseline as the last non-missing value before first dose, compute change from baseline and percent change, and apply observation carry forward.

Learn to count events and unique subjects for adverse events using proc freq or sql, handling term hierarchy and severity when summarizing by combination of variables.

Explore statistical procedures, from univariate descriptive summaries to categorical frequency analysis, then apply inferential tests to compare mean differences between groups and extract data with the odds option.

Learn how to perform a two-sample t-test to compare means across two groups, assess equal variances, interpret p-values, and automate results extraction using ODS trace and ODS output.

Learn how the paired t-test evaluates whether the average difference between post dose and baseline measurements differs from zero, accounting for correlation within the same subject.

Explore macro programming fundamentals, including global and local macro variables, how they interact, defining and calling macros, and macro options for debugging and efficient development with positional and keyword parameters.

Explore macro parameter types—positional parameters that follow order and have no defaults, and keyword parameters with defaults—and learn how options like imprint and simple reveal code and conditional results.

Learn to create standard reports with proc report, using columns and define statements, split headers, titles and footnotes, and breaks, while preferring data steps over compute blocks for accuracy.