Healthcare & Life Sciences · Oncology · Clinical Intelligence
Structuring Oncology Clinical Trial Abstracts into Reliable, Analysis-Ready Evidence
Conference abstracts published across multiple years and oncology indications – each with inconsistent structure, variable outcome reporting, and no common framework for cross-trial comparison. The client needed reliable evidence, not a literature dump.
The Situation
A large body of published data. None of it structured for cross-trial analysis.
A US pharma client working in oncology needed to synthesise clinical trial evidence from published conference abstracts across multiple indications, years, and trial phases. The problem: abstracts are written for individual publication, not for comparative analysis. Outcome definitions varied. Patient population characteristics were described inconsistently. Reporting conventions differed by journal, conference, and year.
The result was a large body of published data that couldn't be reliably compared, aggregated, or used as the basis for commercial or regulatory decisions. The client needed someone who could structure that data into a standardised, analysis-ready evidence asset – not summarise it, but actually standardise it to enable rigorous comparison.
Engagement at a glance
Client
US-based pharma company, oncology division
Data scope
100+ oncology conference abstracts, multiple indications
Deliverable
Standardised evidence database, analysis-ready for cross-trial comparison
Use case
Commercial strategy, medical affairs, regulatory evidence review
Published evidence isn't the same as usable evidence. The gap between them is significant.
Inconsistent abstract structure
Abstracts are written for individual publication – not for comparative analysis. Different conferences, journals, and trial sponsors use different section structures, terminology, and levels of methodological detail. There was no common schema for extracting data in a way that enabled apples-to-apples comparison.
Variable outcome reporting
The same endpoint – overall survival, progression-free survival, objective response rate – was reported at different timepoints, with different statistical approaches, and with varying degrees of subgroup disclosure across trials. Without standardisation, pooling or comparing outcomes across trials introduced systematic error.
Decision-critical data buried in text
The data that actually mattered for commercial and regulatory decisions – patient subgroup performance, dose-response profiles, safety comparisons – was typically buried in narrative text rather than presented in extractable table format. Surfacing it required clinical domain knowledge, not just data extraction.
Standardise before synthesising. The interpretation is only as good as the data structure underneath it.
Standardised extraction schema design
Designed a standardised extraction schema covering trial design, patient population characteristics, primary and secondary endpoints, statistical methods, safety data, and subgroup findings – with field definitions specific enough to ensure consistent extraction across reviewers and abstract styles.
Structured interpretation of text-embedded data
For each abstract, decision-critical data buried in narrative text was surfaced and converted into structured fields. This required oncology domain knowledge to correctly interpret clinical language, handle ambiguous reporting, and flag where data was genuinely unavailable versus where it had been reported in non-extractable form.
Outcome standardisation across trials
Outcome metrics reported at different timepoints or using different statistical conventions were standardised to a common reporting framework – flagging where comparisons required methodological caveats and where they could be made directly. This gave the client a reliable basis for cross-trial comparison without obscuring the underlying data quality variation.
100+ abstracts standardised. Cross-trial analysis enabled. Evidence ready for commercial and regulatory use.
Abstracts structured
100+
Oncology conference abstracts extracted, structured, and standardised across multiple indications and trial phases
Cross-trial analysis
Enabled
Common extraction schema enables reliable apples-to-apples comparison across trial populations, endpoints, and time horizons
Use cases unlocked
Commercial + regulatory
Standardised evidence assets now usable for commercial strategy, medical affairs briefings, and regulatory evidence review
Working with a large body of clinical or scientific evidence?
We have deep healthcare and life sciences analytical capability – from clinical trial intelligence to commercial strategy for pharma and medtech clients.