Summarizing and presenting data from clinical trials

Clinical trials rely on rigorous data preparation and analysis to ensure robust, reliable outcomes. Key components include defining analysis populations, handling missing data and evaluating primary and secondary endpoints. Analysis populations, such as intent-to-treat and per-protocol, play a pivotal role in interpreting treatment efficacy under both real-world and ideal conditions. Handling of missing data, a critical challenge, employs techniques such as multiple imputation and maximum likelihood estimation to minimize bias and preserve validity. Efficacy data analysis revolves around predefined endpoints, with primary endpoints driving trial success and regulatory approval, and secondary endpoints providing broader insights into treatment effects. Subgroup and longitudinal analyses offer nuanced understandings of differential treatment effects and time-based outcomes, leveraging statistical tools such as mixed-effects models and Kaplan–Meier curves. Safety analyses, including adverse event reporting and time-to-event models, are essential for assessing treatment risks. Comparative safety analysis evaluates adverse events, serious adverse events and risk–benefit balances between treatments using methods such as logistic regression and Cox proportional hazards models. By integrating these methodologies, clinical trials provide comprehensive evaluations of treatments, guiding regulatory decisions and advancing medical knowledge. This systematic approach ensures that findings are both scientifically rigorous and clinically relevant.