AI for Biostatisticians

What you do today

Estimate required sample size based on effect size, variability, power, alpha — account for dropout, interim analyses, adaptive design elements

AI that applies

AI simulates thousands of trial scenarios to optimize sample size under various assumptions about effect size, dropout rates, and enrollment patterns

How it works

For perform sample size calculation, the system draws on the relevant operational data and applies the appropriate analytical models. The simulation engine runs thousands of scenarios by varying each uncertain input across its probability range, building a distribution of outcomes that quantifies the risk. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

Ensure SDTM and ADaM datasets comply with CDISC standards, review dataset specs with data management, validate derivation logic

AI that applies

AI validates CDISC compliance automatically, flags deviations from SDTM/ADaM standards, and suggests conformant variable mappings

How it works

The system ingests SDTM/ADaM standards as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

Independently program key analyses as QC check against primary programmer, reconcile discrepancies, ensure double-programming compliance

AI that applies

AI assists with independent QC programming, automatically compares outputs, and identifies discrepancies at the cell level in TFLs

How it works

For validate statistical programming output, the system identifies discrepancies at the cell level in tfls. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

Define stratification factors, block sizes, randomization ratios — balance scientific rigor with operational feasibility across trial sites

AI that applies

AI optimizes stratification and dynamic randomization (minimization) parameters to maximize balance across prognostic factors

How it works

For design randomization scheme, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

Execute pre-planned interim analysis, prepare DSMB report with futility/efficacy boundaries, present results under strict unblinding protocols

AI that applies

AI automates DSMB report generation from clinical database, performs group sequential calculations, and generates standardized safety tables

How it works

The system ingests clinical database as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — standardized safety tables — surfaces in the existing workflow where the practitioner can review and act on it.

What you do today

Execute the pre-specified primary analysis from the SAP, run sensitivity analyses, produce TFLs (tables, figures, listings) for the clinical study report

AI that applies

AI-assisted programming generates validated TFLs faster, automatically QC's output against SAP specifications, and flags discrepancies

How it works

For perform primary efficacy analysis for csr, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — validated TFLs faster — surfaces in the existing workflow where the practitioner can review and act on it.

What you do today

Define primary endpoint analysis, handling of missing data, multiplicity adjustments, sensitivity analyses, subgroup analyses — document in ICH E9-compliant SAP

AI that applies

AI drafts SAP sections from protocol parameters, suggests appropriate statistical methods based on endpoint type, and ensures ICH E9(R1) compliance

How it works

The system ingests protocol parameters as its primary data source. A language model processes the input by identifying relevant context, generating appropriate responses, and structuring the output to match the expected format and domain conventions. The output is a recommended plan or schedule that accounts for the identified constraints and optimization criteria.

What you do today

Implement missing data methods (MMRM, multiple imputation, pattern-mixture models, tipping point analyses) per ICH E9(R1) estimand framework

AI that applies

AI automates sensitivity analyses across multiple missing data assumptions, generates tipping point analysis results, and visualizes impact on conclusions

How it works

The system ingests across multiple missing data assumptions as its primary data source. A language model processes the input by identifying relevant context, generating appropriate responses, and structuring the output to match the expected format and domain conventions. The output — tipping point analysis results — surfaces in the existing workflow where the practitioner can review and act on it.

What you do today

Advise on endpoint selection, design trade-offs (parallel vs crossover, superiority vs non-inferiority), power implications of design choices

AI that applies

AI simulates trial outcomes under different design options, quantifying trade-offs in power, cost, and timeline for each alternative

How it works

The system ingests clinical data — patient records, lab results, vitals, and care history from the EHR. A language model processes the input by identifying relevant context, generating appropriate responses, and structuring the output to match the expected format and domain conventions. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What you do today

Anticipate FDA statistical reviewer questions, prepare backup analyses, build defense for your analytical approach

AI that applies

AI analyzes FDA review histories for similar drugs and endpoints, identifies common statistical objections, and suggests pre-emptive analyses

How it works

The system ingests FDA review histories for similar drugs and endpoints as its primary data source. A language model processes the input by identifying relevant context, generating appropriate responses, and structuring the output to match the expected format and domain conventions. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.