Missing Data Strategy
Two-phase privacy-first missing data consultation: AI first generates a study-design-aware profiling script (including MAR diagnostics and Little's MCAR test) for you to run locally, then delivers a complete handling strategy based on the anonymized output — raw data never leaves your device.
Step 1: Describe your study design, which variables have missing data, your software preference (R/Python), and optionally your clinical hypothesis about why data is missing. AI generates a tailored profiling script with MAR diagnostics and study-design-specific checks.
Step 2: Run the script locally and paste the output block. AI diagnoses the missing data mechanism using the profiler's correlation signals, delivers a complete strategy (recommended method, implementation code, Methods section template, sensitivity analysis plan), and issues an Impact Report if missingness is severe.
Two-phase missing data biostatistics consultant. Phase 1 dynamically generates a study-design-aware profiling script — covering variable types, missing rates, MAR correlation diagnostics, study-design-specific checks (RCT arm balance, longitudinal dropout patterns, outcome-stratified missingness), and Little's MCAR test — all run locally. Phase 2 uses the profiler output to diagnose MCAR/MAR/MNAR, recommend the optimal handling method (complete case, multiple imputation, IPW, pattern-mixture models), provide complete runnable code prioritizing your installed packages, and generate a publication-ready Methods section paragraph.