Clinical Trials

AY2024/2025 Semester 2

Clinical trials are used to evaluate the safety, efficacy and feasibility of a treatment or a drug in human. It is a critical step in commercialising any pharmaceutical products. This course focuses on the statistical aspects of conducting clinical trials. We will introduce drug development process, best practices and regulatory requirements in the industry. Various trial designs and analysis methods at different phases of drug development will be discussed. This course is essential for students who plan to work with clinical or medical data. You will acquire knowledge in analysing clinical data and in interpreting the analysis results. You will also learn to appreciate medical research papers. Course Content: General Introduction to Clinical Trials: 1. Definition of clinical trials 2. Regulatory requirements and guidelines 3. Good clinical practice 4. Drug development process 5. Phases of clinical trials Mixed Effects Model: 1. Fixed and random effects 2. Crossover design, period and sequence effects of a trial 3. Repeated measures and effects of missing data 4. Total, Inter- and intra-subject variances 5. Using SAS?s Proc Mixed procedure 6. Variance and covariance structures Basic Pharmacokinetics: 1. Pharmacokinetic and Pharmacodynamic 2. Absorption, Distribution, Metabolism and Elimination of drugs 3. Pharmacokinetic parameters and their estimation methods 4. Log-transformation, log-normal distribution and coefficient of variation Treatment Comparison in Various Designs: 1. Bioequivalence, Superiority, and non-inferiority trials 2. Two one-sided tests 3. Equivalence and non-inferiority margins 4. Dose proportionality study, confidence interval approach 5. Food effects, hepatic or renal impairment, age or Gender comparisons 6. Drug-Drug interaction trials Nonparametric Methods: 1. Sign test for median 2. Wilcoxon signed-rank test 3. Wilcoxon rank sum test 4. Confidence interval for median and difference of medians Longitudinal Data Analysis: 1. Characteristics of Longitudinal Data 2. Change from baseline analysis 3. Analysis of covariance approach 4. Mixed effects approach with various covariance structures Sample Size and Power Calculations: 1. Clinically meaningful difference and primary study endpoint 2. Sample size estimation and power calculation for bioequivalence, superiority and non-inferiority trials