Cohort explorer
Review eligibility rules, attrition, baseline characteristics, missingness and follow-up together. Export the selected cohort definition with each summary.
Useful for: feasibility discussions and reviewing analysis populations.
Clarity in data.Confidence in delivery.
Independent support for life science teams.Making evidence clear and reproducible.Working with you from brief to delivery.
I’m Sai Prasad Jandhyala. Since 2006, I’ve worked across clinical programming and evidence generation. My background spans oncology, immunology, infectious diseases, neuroscience and rare diseases. I help teams connect reliable analysis, practical applications and clear technical review.
FROM DATA TO DECISIONSConnected expertise
The question.
The code.
The solution.
- 01Understand the dataClinical trials · Registries · Real-world data
- 02Build the analysisSAS & R · SDTM / ADaM · TLFs
- 03Make it usefulShiny & Python apps · Review tools · Automation
- 04Review the evidenceQuality checks · Traceability · AI / ML assurance
STATISTICAL PROGRAMMINGSAS / R / PYTHONCDISCSHINY & APPLICATIONSAI / ML ASSURANCE
EXPERTISE, APPLIED
Specialist support.
Practical outcomes.
From a defined programming work package to a focused technical review, work directly with the person delivering it.
01 / EXPERTISE
SAS & CDISC programming
Hands-on SAS programming for clinical reporting teams that need clear specifications, reliable outputs and an experienced collaborator.
Explore service 02 / EXPERTISER & Shiny dashboards
Purpose-built R workflows and Shiny applications that bring clinical, operational and analytical questions into focus.
Explore service 03 / EXPERTISEPython & R applications
Focused Python and R/Shiny application development for statistical programming and RWE teams, scoped around your workflow and agreed with your statistical leads.
Explore service 04 / EXPERTISEReal-world evidence programming
Programming support for observational research, with explicit cohort definitions, provenance and reproducible transformations.
Explore service 05 / EXPERTISEData science & reproducibility
Practical data science with attention to the assumptions, evaluation and engineering around the model.
Explore service 06 / EXPERTISEAI & ML implementation reviews
Independent technical assessment of AI and machine-learning implementations, from data preparation to human oversight.
Explore serviceSince 2006Clinical programming experience
Submission supportSDTM · ADaM · TLFs · ISS / ISE
RWE & applicationsStandards, automation and dashboards
2 PHUSE papersPractical knowledge, shared
APPLICATIONS FOR STATISTICAL PROGRAMMING
Turn repeated questions
into reusable tools.
Focused Python and R/Shiny applications, designed with your statisticians and programming team around a defined workflow.
Dataset & output checker
Compare dataset versions, highlight metadata changes, review SAS logs and track discrepancies against a specification.
Useful for: repeatable QC and focused vendor reviews.
Analysis workbench
Run agreed descriptive summaries and reporting templates through controlled inputs, with visible denominators, data versions and downloadable outputs.
Useful for: consistent recurring analyses and transparent handover.
Example build scopes for discussion. Each application needs agreed calculations, validation and access controls before operational use.
Explore Python & R application supportRESPONSIBLE INNOVATION
Moving AI forward.
With the right checks.
Speed matters. So do traceability, validation and human judgement. I help organisations examine the evidence behind their AI and ML implementations.
Explore AI assurance01
Define the intended use
02
Examine data and evaluation
03
Test failure and oversight
04
Prioritise practical improvements
IDEAS FROM THE WORKBENCH
Explore the latest thinking.
Article · AI & ML
AI-assisted programming needs an evidence trail
The valuable question is not how quickly a model writes code. It is how confidently a team can review the result.
Read insightArticle · R & Shiny
From a Shiny prototype to a trusted review tool
A dashboard is only useful when users understand the data, the calculations and the decisions it supports.
Read insightArticle · Real-world evidence
Real-world data: define the cohort before the model
Index dates, eligibility and follow-up can change the answer before a statistical model is fitted.
Read insight