Statistical Methods

Course Overview

This course introduces the key statistical principles and methods used in clinical trials, focusing on their practical application in data analysis and reporting. You will learn how to interpret statistical analysis plans (SAP), understand common efficacy and safety endpoints, and link statistical concepts to ADaM datasets and TFL generation.

Learning Objectives

• Understand the role of statistics in clinical trial design and analysis.

• Learn about hypothesis testing, confidence intervals, and p-values.

• Interpret Statistical Analysis Plans (SAP) and their relation to programming tasks.

• Understand common statistical models used in clinical trials.

• Link statistical concepts to ADaM datasets and TFL outputs.

• Gain familiarity with efficacy and safety analysis methods.

Course Modules

Module 1: Fundamentals of Biostatistics

• Key concepts: population, sample, variable types

• Descriptive statistics and data visualization

• Understanding variability and distributions

Module 2: Study Design and Randomization

• Clinical trial phases and design types

• Randomization and blinding techniques

• Sample size and power calculations

Module 3: Statistical Inference

• Hypothesis testing framework

• Confidence intervals and significance levels

• Parametric vs. non-parametric tests

Module 4: Efficacy and Safety Analysis

• Analysis of continuous and categorical endpoints

• Time-to-event (survival) analysis

• Adverse event frequency and severity tables

Module 5: Advanced Statistical Models

• ANCOVA and mixed models for repeated measures (MMRM)

• Logistic and Cox regression models

• Handling missing data and sensitivity analysis

Module 6: Linking Statistics with Programming

• Translating SAP specifications into ADaM derivations

• Creating TFLs that match statistical objectives

• Ensuring statistical consistency in outputs

Key Features

• 📘 Comprehensive coverage of clinical trial statistics

• 💻 Practical examples using real-world datasets

• 🔍 Direct linkage between statistical theory and ADaM programming

• ✅ Clear explanations tailored for SAS programmers

Who Should Attend

• Clinical SAS programmers working on ADaM or TFLs

• Data analysts transitioning into clinical research

• Biostatistics or epidemiology graduate students

• Clinical project team members who interpret statistical results

Duration

Approx. 8–10 hours (including hands-on case exercises)

What You’ll Gain

By completing this course, you’ll be able to understand statistical concepts in clinical trials, interpret SAPs, and apply statistical reasoning directly in your ADaM programming and TFL generation work.