In the pharmaceutical industry, statistical methods play a crucial role in ensuring the accuracy, reliability, and compliance of data across drug development, clinical trials, and manufacturing. Here are some key statistical techniques used in pharma:
Descriptive Statistics – Summarizes data using mean, median, standard deviation, and graphical representations.
Inferential Statistics – Draws conclusions from sample data using hypothesis testing, confidence intervals, and p-values.
Regression Analysis – Helps understand relationships between variables, essential for dose-response modeling and stability studies.
Analysis of Variance (ANOVA) – Compares means across multiple groups to assess formulation performance and process variations.
Survival Analysis – Estimates time-to-event data, widely used in clinical trials for overall survival and progression-free survival.
Bayesian Statistics – Incorporates prior knowledge into data analysis, increasingly applied in adaptive clinical trials.
Design of Experiments (DOE) – Optimizes processes and formulations using factorial and response surface methodologies.
Multivariate Analysis – Handles complex datasets in quality control, biomarker identification, and process validation.
Monte Carlo Simulation – Assesses risk and variability in drug manufacturing and regulatory decision-making.
Machine Learning & AI in Pharma – Uses statistical algorithms for predictive modeling, patient stratification, and real-world evidence analysis.
From early-stage R&D to post-market surveillance, statistics remain the backbone of data-driven decision-making in pharma. Embracing these techniques enhances drug development efficiency, regulatory compliance, and patient safety.
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Resource Person: Pratik Pawar