Clinical Research Data Analysis Made Simple
Transform complex medical data into actionable insights with AI-powered statistical analysis tools designed for healthcare professionals.
Clinical research generates massive amounts of data - from patient demographics and biomarkers to treatment outcomes and adverse events. Making sense of this data requires sophisticated statistical analysis, but traditional tools often create barriers between researchers and insights.
With AI-powered analysis tools, healthcare professionals can now perform complex statistical analyses without wrestling with code or confusing interfaces. Let's explore how modern data analysis transforms clinical research workflows.
Why Healthcare Professionals Choose AI-Powered Analysis
Transform your clinical research workflow with tools designed specifically for medical data analysis.
HIPAA-Compliant Security
Enterprise-grade security ensures patient data remains protected while enabling powerful analysis capabilities for research teams.
Statistical Test Automation
Automatically run appropriate statistical tests based on your data type - from t-tests and ANOVA to survival analysis and regression modeling.
Real-Time Collaboration
Share findings with research teams instantly. Multiple investigators can review results, add annotations, and iterate on analysis simultaneously.
Regulatory Reporting
Generate publication-ready tables, figures, and statistical summaries that meet regulatory requirements for clinical trial submissions.
Missing Data Handling
Advanced imputation methods and sensitivity analyses handle missing data appropriately, maintaining statistical validity in your research.
Power Analysis Tools
Calculate sample sizes, detect effect sizes, and perform post-hoc power analyses to ensure your studies have adequate statistical power.
Clinical Research Analysis in Action
See how healthcare professionals use AI-powered tools to analyze clinical data and accelerate medical discoveries.
Phase II Oncology Trial Analysis
A research team analyzing tumor response rates across 240 patients discovered that adding biomarker stratification increased response prediction accuracy by 34%. The AI assistant automatically suggested Kaplan-Meier survival curves and Cox proportional hazards modeling, revealing that patients with high biomarker expression had significantly improved progression-free survival (HR=0.62, 95% CI: 0.41-0.93, p=0.021).
Cardiovascular Outcomes Study
Researchers examining heart failure readmission rates across 1,200 patients found that medication adherence was the strongest predictor of 30-day readmission. Using automated logistic regression analysis, they identified that patients with >90% adherence had 43% lower odds of readmission (OR=0.57, 95% CI: 0.39-0.84, p=0.004), leading to implementation of an enhanced medication monitoring program.
Pediatric Safety Analysis
A safety monitoring committee analyzing adverse events in a pediatric vaccine trial needed to quickly assess whether fever rates differed by age group. The AI-powered analysis automatically stratified the 850 participants by age cohorts and performed chi-square tests, revealing that fever incidence was significantly higher in the 12-24 month group (32.4%) compared to older children (18.7%, p<0.001), prompting dosage adjustments for younger patients.
Multi-Site Diabetes Study
Investigators pooling data from 15 clinical sites needed to account for site-to-site variability in HbA1c measurements. Using mixed-effects modeling, they discovered that while the overall treatment effect was significant (mean difference: -0.8%, p<0.001), there was substantial heterogeneity between sites (I² = 67%), leading to site-specific protocol modifications and improved standardization procedures.
Rare Disease Natural History Study
Researchers tracking disease progression in 89 patients with a rare genetic disorder used longitudinal analysis to model symptom severity over time. The AI assistant suggested using generalized estimating equations to handle missing data and repeated measures, revealing that disease progression followed a non-linear pattern with acceleration after 18 months, informing optimal timing for therapeutic interventions.
From Raw Data to Published Results
Transform clinical data into publication-ready analyses with an intuitive workflow designed for healthcare professionals.
Import Clinical Data
Upload data from any source - EDC systems, REDCap, Excel files, or CSV exports. The AI automatically detects data types, identifies potential issues, and suggests data cleaning steps.
Automated Analysis Selection
Describe your research question in plain English. The AI recommends appropriate statistical tests based on your study design, data distribution, and research objectives.
Interactive Exploration
Explore your data with dynamic visualizations. Create forest plots, survival curves, and correlation matrices with natural language commands like 'show me survival by treatment group.'
Statistical Validation
The AI automatically checks assumptions, performs diagnostic tests, and suggests alternative approaches when assumptions are violated, ensuring robust statistical conclusions.
Generate Reports
Create comprehensive statistical reports with tables, figures, and interpretations that meet journal and regulatory requirements. Export directly to Word or LaTeX formats.
Ready to Transform Your Clinical Research?
Comprehensive Statistical Toolkit
Access the full range of statistical methods commonly used in clinical research, from basic descriptive statistics to advanced modeling techniques.
Descriptive Statistics
Generate comprehensive demographic tables, summary statistics, and data quality assessments with automatic handling of different variable types and missing data patterns.
Hypothesis Testing
Perform t-tests, chi-square tests, Mann-Whitney U, Kruskal-Wallis, and other non-parametric tests with automatic assumption checking and effect size calculations.
Survival Analysis
Create Kaplan-Meier curves, perform log-rank tests, and build Cox proportional hazards models for time-to-event outcomes common in clinical trials.
Regression Modeling
Build linear, logistic, and Poisson regression models with automatic variable selection, interaction testing, and model validation procedures.
Repeated Measures
Analyze longitudinal data using mixed-effects models, GEE, and other methods designed for correlated observations in clinical studies.
Meta-Analysis
Combine results from multiple studies using fixed and random-effects models, with forest plots and heterogeneity assessment tools.
Handle Any Type of Clinical Data
Clinical research involves diverse data types, each requiring specialized analytical approaches. Our AI-powered platform automatically recognizes and appropriately analyzes different types of medical data:
Patient Demographics and Baseline Characteristics
Age, gender, race, ethnicity, medical history, concomitant medications, and baseline disease severity scores. The system automatically generates Table 1 demographic summaries and performs balance testing for randomized trials.
Laboratory Values and Biomarkers
Blood chemistry panels, hematology counts, immunology markers, and specialized bioassays. Includes automatic reference range checking, outlier detection, and longitudinal trending analysis.
Clinical Outcomes and Endpoints
Primary and secondary efficacy endpoints, composite outcomes, patient-reported outcomes (PROs), and quality of life measures. Supports both continuous and categorical endpoint analysis.
Safety and Adverse Events
Adverse event classifications, severity grading, causality assessments, and serious adverse event reporting. Automated safety signal detection and regulatory reporting tables.
Time-to-Event Data
Survival endpoints, time to progression, duration of response, and other time-based outcomes with appropriate censoring handling and competing risk analysis.
Whether you're working with complex statistical models or simple comparative analyses, the platform adapts to your data structure and research objectives.
Meet Regulatory Requirements
Ensure your clinical research analysis meets the highest standards for regulatory submissions and peer-reviewed publications.
FDA Guidelines Compliance
Statistical analysis plans and outputs follow FDA guidance documents for clinical trials, including ICH E9 statistical principles and E3 structure and content guidelines.
GCP Documentation
Maintain complete audit trails with timestamped analysis logs, version control, and electronic signatures meeting Good Clinical Practice requirements.
Statistical Analysis Plans
Generate comprehensive SAPs with pre-specified analysis methods, handling of missing data, and multiplicity adjustments to ensure statistical integrity.
CONSORT Reporting
Automatically generate CONSORT flow diagrams and required reporting elements for randomized controlled trials and systematic reviews.
Frequently Asked Questions
How does AI-powered analysis ensure statistical validity in clinical research?
The AI system is built on established statistical principles and automatically performs assumption checking, diagnostic tests, and validation procedures. It follows FDA guidance and ICH guidelines, ensuring that all analyses meet regulatory standards. The system also provides transparent documentation of all analytical decisions and maintains complete audit trails.
Can I import data from electronic data capture (EDC) systems?
Yes, the platform supports data import from all major EDC systems including Medidata Rave, Oracle Clinical, REDCap, and others. Data can be imported via direct API connections, CSV exports, or SAS transport files while maintaining data integrity and audit trails.
How are missing data and protocol deviations handled?
The system automatically detects missing data patterns and suggests appropriate imputation methods based on the missingness mechanism. For protocol deviations, it supports multiple analysis populations (ITT, PP, mITT) with clear documentation of inclusion/exclusion criteria and sensitivity analyses.
What statistical methods are available for survival analysis?
The platform includes comprehensive survival analysis tools: Kaplan-Meier estimation, log-rank tests, Cox proportional hazards regression, parametric survival models, competing risks analysis, and landmark analysis. All methods include automatic assumption checking and diagnostic plots.
How does the platform handle multi-center studies?
Multi-center studies are supported through mixed-effects modeling, stratified analyses, and center-effect adjustments. The system can pool data across centers while accounting for between-center variability and testing for treatment-by-center interactions.
Are the analysis outputs suitable for regulatory submissions?
Yes, all outputs are generated in formats suitable for regulatory submissions including FDA, EMA, and other international authorities. Tables, listings, and figures follow standard formatting guidelines, and statistical reports include all required elements for regulatory review.
How does the platform ensure data security and HIPAA compliance?
The platform implements enterprise-grade security with encryption at rest and in transit, role-based access controls, and comprehensive audit logging. It is designed to meet HIPAA, GCP, and other regulatory requirements for handling sensitive clinical data.
Can I generate publication-ready figures and tables?
Absolutely. The system generates high-quality, publication-ready tables, figures, and forest plots that meet journal standards. All outputs can be customized for specific journal requirements and exported in multiple formats including PDF, PNG, and vector graphics.
Sourcetable Frequently Asked Questions
How do I analyze data?
To analyze spreadsheet data, just upload a file and start asking questions. Sourcetable's AI can answer questions and do work for you. You can also take manual control, leveraging all the formulas and features you expect from Excel, Google Sheets or Python.
What data sources are supported?
We currently support a variety of data file formats including spreadsheets (.xls, .xlsx, .csv), tabular data (.tsv), JSON, and database data (MySQL, PostgreSQL, MongoDB). We also support application data, and most plain text data.
What data science tools are available?
Sourcetable's AI analyzes and cleans data without you having to write code. Use Python, SQL, NumPy, Pandas, SciPy, Scikit-learn, StatsModels, Matplotlib, Plotly, and Seaborn.
Can I analyze spreadsheets with multiple tabs?
Yes! Sourcetable's AI makes intelligent decisions on what spreadsheet data is being referred to in the chat. This is helpful for tasks like cross-tab VLOOKUPs. If you prefer more control, you can also refer to specific tabs by name.
Can I generate data visualizations?
Yes! It's very easy to generate clean-looking data visualizations using Sourcetable. Simply prompt the AI to create a chart or graph. All visualizations are downloadable and can be exported as interactive embeds.
What is the maximum file size?
Sourcetable supports files up to 10GB in size. Larger file limits are available upon request. For best AI performance on large datasets, make use of pivots and summaries.
Is this free?
Yes! Sourcetable's spreadsheet is free to use, just like Google Sheets. AI features have a daily usage limit. Users can upgrade to the pro plan for more credits.
Is there a discount for students, professors, or teachers?
Currently, Sourcetable is free for students and faculty, courtesy of free credits from OpenAI and Anthropic. Once those are exhausted, we will skip to a 50% discount plan.
Is Sourcetable programmable?
Yes. Regular spreadsheet users have full A1 formula-style referencing at their disposal. Advanced users can make use of Sourcetable's SQL editor and GUI, or ask our AI to write code for you.
Transform Your Clinical Research Today
Join healthcare professionals worldwide who trust AI-powered analysis for faster, more reliable clinical research insights.