All modules — detailed view
Your RWE methodology compass
Included in all plans
What you get
Interactive guidelines directory with canonical references, methodology summaries, and regulatory guidance for any RWE question.
Ideal for:
Any team starting a new study or looking for methodological orientation.
Landscape intelligence in minutes
1 credits / run
What you get
Comprehensive data source inventory, literature gap analysis, competitor study mapping, and regulatory precedent review — structured as a 5–10 page landscape brief.
Ideal for:
Evidence strategists, medical affairs teams, early-stage study planning.
From idea to concept sheet in one run
15 credits / run
What you get
A 3–7 page HARPER-aligned study concept sheet covering objectives, PICOTS framework, study design rationale, preliminary data source selection, and regulatory context.
Ideal for:
RWE scientists, pharmacoepidemiologists, regulatory affairs teams.
Go/no-go before you commit a budget
30 credits / run
What you get
A 5–30 page feasibility report with patient count estimates, ICD code coverage analysis, database scoring against study parameters, risk evaluation, and a clear Go/No-Go recommendation — plus a database landscaping spreadsheet.
Ideal for:
Study teams evaluating data sources, CRO partners, sponsors.
Publication-ready protocol in hours, not weeks
40 credits / run
What you get
A 40–70 page HARPER + EMA GVP VIII-compliant study protocol covering background, objectives, methodology, statistical considerations, ethical provisions, and two HARPER tables justifying every design choice.
Ideal for:
Pharmacoepidemiologists, regulatory teams, ENCePP submissions.
Statistical rigour, ICH E9(R1) aligned
35 credits / run
What you get
A 30–50 page statistical analysis plan with estimand framework, sample size, analysis populations, exposure/outcome/confounder evaluation, subgroup and sensitivity analyses — plus 10–12 study-tailored TFL shells in Excel.
Ideal for:
Biostatisticians, statistical programmers, regulatory submissions.
Analysis-ready code, aligned to your SAP
30 credits / run
What you get
A complete statistical code package in R, SAS, Python, or Stata — annotated, production-ready, aligned to your SAP and data model (OMOP, FHIR, claims). Delivered as a ZIP archive with macro libraries and execution scripts.
Ideal for:
Statistical programmers, data scientists, OMOP/FHIR specialists.
From raw data to analysis-ready dataset
Coming soon
What you get
Data preparation, cleaning, and structuring for RWE analysis across OMOP, FHIR, claims, EHR, and registry sources.
Ideal for:
Data engineers, analysts, OMOP specialists.
Primary and sensitivity analyses, output-ready
Coming soon
What you get
Run primary and sensitivity analyses with output-ready tables, figures, and listings aligned to your SAP.
Ideal for:
Biostatisticians, data analysts, study teams.
Clinical study report in regulatory format
50 credits / run
What you get
A 100–200 page clinical study report structured per ICH E3, RECORD, or GVP Module VIII, integrating protocol, results tables, and narrative sections.
Ideal for:
Regulatory affairs, medical writing teams, sponsor submissions.
From report to publication-ready manuscript
20 credits / run
What you get
A polished first draft manuscript structured for your target journal and reporting guideline (STROBE, RECORD-PE, CONSORT, PRISMA), with inline citations and discussion section.
Ideal for:
Medical writers, scientists, publication planning teams.
Expert critique in seconds
2 credits / run
What you get
A structured critical appraisal of any manuscript, protocol, SAP, or report — identifying methodological strengths, limitations, reporting gaps, and quality scores against CASP, STROBE, CONSORT, PRISMA, or GRADE frameworks.
Ideal for:
Peer reviewers, quality teams, regulatory assessors.