Statistical analysis and significance tests for clinical trial data

Abstract

The analysis of clinical trial data is vital for determining the true effects of treatments and differentiating these effects from random variation. Two key statistical methodologies are discussed: descriptive and inferential. Descriptive statistics provide insights by summarizing participant characteristics, treatment outcomes, and variable distributions using measures such as the mean, median, standard deviation and interquartile range. These summaries set the stage for hypothesis testing and assumption validation. Inferential statistics extend this foundation by enabling generalizations about a broader population, employing methods such as hypothesis testing, confidence intervals and regression models. Hypothesis testing evaluates the evidence for treatment effects, often using statistical tests such as t-tests, analysis of variance or chi-squared tests, while confidence intervals quantify the precision of these effects. Survival analysis, such as Kaplan–Meier curves and Cox models, is employed for time-to-event data. Adjusting for covariates is crucial for controlling confounding factors and is often paired with methods to manage multiple comparisons, such as Bonferroni corrections and false discovery rate (FDR) procedures. Proper power calculations ensure adequate sample sizes to detect meaningful effects, minimizing type I and type II errors. This comprehensive approach strengthens the reliability of clinical trial conclusions, supporting evidence-based decision-making in medical research.