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Clinical program statistical analysis is a critical component of clinical research used to evaluate the safety, efficacy, and outcomes of medical interventions. It involves applying statistical methods to clinical trial data to support evidence-based decision-making and regulatory compliance.
The primary objectives of statistical analysis in a clinical program are to:
Assess treatment effectiveness and safety Compare outcomes between study groups Identify trends, patterns, and risk factors Ensure data integrity and scientific validity Support regulatory submissions and publicationsStatistical analysis begins with a well-defined study design, including:
Study type (randomized, controlled, observational) Sample size determination and power calculation Definition of endpoints (primary, secondary, exploratory) Data collection methods and validation checksCommon statistical techniques used in clinical programs include:
Descriptive statistics (mean, median, standard deviation) Inferential statistics (t-tests, chi-square tests, ANOVA) Regression analysis Survival analysis (Kaplan–Meier, Cox proportional hazards) Interim and final analysesData analysis is conducted according to predefined protocols to ensure objectivity. Results are interpreted in the context of clinical relevance, not just statistical significance. Sensitivity analyses may be performed to validate robustness.
Statistical findings are documented in clinical study reports (CSRs), ensuring compliance with regulatory guidelines such as ICH-GCP, FDA, and EMA standards. Transparency and reproducibility are key requirements.
Clinical program statistical analysis ensures reliable evaluation of clinical trial data, supports regulatory approval, and contributes to the development of safe and effective healthcare solutions.
Alicanto offer a wide range of services in the areas of analysis, design, development, and maintenance of various IT systems.
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