Statistical Analysis
Statistical analysis is crucial as it ensures that the study results are valid, reliable, and meaningful. Without proper statistical methods, clinical trials could produce misleading conclusions, putting patients at risk and wasting resources.
At WEP, our biostatistics partners play a crucial role in ensuring the integrity, accuracy, and compliance of clinical trial data. With expertise in study design, advanced statistical modeling, and regulatory submissions, we provide the analytical backbone that transforms raw data into reliable and actionable insights, supporting successful drug development and regulatory approvals.
Comprehensive Data Analysis and Interpretation Services
Our partner team applies cutting-edge statistical methods and software to analyze clinical trial data and generate reliable conclusions, ensuring robust assessments of safety and efficacy.
Descriptive Statistics & Exploratory Data Analysis (EDA)
Provides an overview of data distribution and identifies potential issues:
- Summarize Baseline Characteristics: Age, gender, medical history, etc.
- Visualize Data: Use histograms, box plots, and scatter plots to check for anomalies.
- Handle Missing Data: Apply imputation techniques if needed.
- Check Assumptions for Statistical Tests: Ensure normality, homogeneity of variance, etc.
Inferential Statistical Analysis
Determines whether treatment effects are real or due to chance:
- Compare Treatment Groups:
- T-tests / ANOVA: Compare means (e.g., drug vs. placebo).
- Chi-square tests: Compare categorical outcomes.
- Survival Analysis (if applicable):
- Kaplan-Meier curves (e.g., time-to-event outcomes in oncology).
- Cox Proportional Hazards Model (adjusting for covariates).
- Regression Analysis:
- Logistic regression for binary outcomes (e.g., response vs. no response).
- Linear regression for continuous outcomes.
- Adjust for Confounding Variables: Use multivariate models to control for bias.
- Assess Statistical Significance: Use p-values (typically p < 0.05) and confidence intervals (CIs).
Sensitivity & Subgroup Analysis
Ensures robustness of findings across different patient populations:
- Analyze Different Subgroups: (e.g., age, gender, disease severity).
- Perform Sensitivity Analyses: Test different statistical approaches to verify results (e.g., excluding outliers, different imputation methods for missing data).
- Check for Consistency of Effects: Ensure treatment benefits apply broadly.
Interim Analysis (if applicable)
Allows early decision-making without compromising the trial’s integrity:
- Conduct Pre-planned Analyses: Check efficacy and safety before trial completion.
- Implement Stopping Rules: Stop trial early for overwhelming success, futility, or safety concerns.
- Use Adaptive Design (if applicable): Modify trial parameters (e.g., increase sample size) based on interim results.
Final Analysis & Interpretation
Translates statistical results into meaningful clinical insights:
- Generate Clinical Study Reports (CSRs): Summarizes findings for regulators.
- Interpret Clinical Relevance: Are the results clinically meaningful beyond statistical significance?
- Perform Risk-Benefit Analysis: Weigh treatment benefits against potential risks.