Data Management & Analytics

Real-world evidence is only as credible as the data management and analytical process that produces it. Observational data from claims databases, electronic health records, disease registries, and primary-care networks contains noise, gaps, and structural variation that interventional trial data does not. Without a rigorous, documented approach to handling that complexity, findings become difficult to defend – to regulators, to HTA bodies, and to internal stakeholders who need to act on the results.

For clinical development teams using RWE to support regulatory submissions, access decisions, or label extensions, the analytical credibility of the data management process is not a methodological detail. It is a determinant of whether the evidence can be used for its intended purpose.

APICES manages real-world data through structured processes aligned with your study design and regulatory context. Our data management and analytics teams are embedded in the same delivery model as observational study design, data sourcing, and medical writing – ensuring that analytical decisions are made with full visibility into where the data came from, how it was accessed, and what its limitations are.

Data Management for Observational Studies

Data management in RWE and HEOR studies involves a distinct set of challenges. Multiple data sources often need to be harmonised. Patient populations are defined algorithmically rather than by enrollment criteria. Missing data is not random. Coding systems vary by country and data source.

APICES addresses these challenges through a data management plan developed alongside the study design and statistical analysis plan. Data sources are profiled before the study begins – variable availability, coding completeness, date granularity, and linkage potential are assessed and documented. Assumptions are explicit, and their impact on analytical outputs is understood before analysis begins.

Data cleaning, transformation, and quality control processes are documented in the data management plan and traceable through to the analytical datasets. This supports regulatory review, audit, and replication – which are increasingly required for RWE submissions under EMA's Real-World Evidence Framework.

Analytical Methods for Real-World Evidence

The analytical approach must match the study design and the regulatory or access context. APICES applies a range of established methodologies:

• Propensity score methods (matching, weighting, stratification) for comparative effectiveness studies
• Target trial emulation frameworks for studies designed to emulate randomised controlled trials using real-world data
• Indirect treatment comparisons (ITCs) and network meta-analysis (NMA) for payer submission dossiers
• Survival analysis and time-to-event modelling for oncology and rare disease programs
• Cost-of-illness, budget impact, and cost-effectiveness modelling for HEOR submissions

Analytical plans are pre-specified, registered where required (e.g., EU PAS Register for PASS studies), and reviewed before analysis begins. Post-hoc analytical changes are documented transparently.

Integration with Reporting and Access Decisions

Data management and analytics at APICES are aligned with downstream reporting requirements from the start. Analytical outputs are structured to support clear interpretation in clinical study reports, HTA dossiers, and peer-reviewed publications. Medical writers are involved in the study design phase so that reporting structures are defined before data collection begins.

This alignment is particularly important for studies that need to meet both regulatory and HTA standards simultaneously – for example, a PASS study that also needs to support AMNOG submission in Germany or HAS evaluation in France.

What You Can Expect

• A pre-specified data management plan aligned with the statistical analysis plan and study design
• Documented data profiling and quality assessment before analysis begins
• Methodologically appropriate analytical approaches, pre-specified and externally reviewable
• Analytical outputs structured for regulatory, HTA, and publication use
• Full traceability from raw data source to reported result
• Integrated delivery with observational study design, data sourcing, and medical writing

Frequently Asked Questions

Which analytical methods does APICES use for RWE?

Propensity score methods, target trial emulation, indirect treatment comparison, network meta-analysis, survival modelling, and cost-effectiveness modelling – selected based on study design and the regulatory or access context.

How does APICES handle multi-source data harmonisation?

Through a documented data management plan that addresses coding system alignment, variable mapping, date granularity, and linkage methodology before analysis. Assumptions are explicit and their impact on findings is assessed.

Are analytical plans pre-registered?

Where required by the study design or regulatory framework – for example, PASS studies submitted to the EU PAS Register – yes. Pre-specification is a standard requirement; post-hoc changes are documented and justified.

Can APICES support HEOR and RWE in the same study?

Yes. Clinical and economic endpoints are managed within a single analytical framework, with output structured to support both regulatory and HTA review simultaneously.

How are analytical outputs reviewed for quality?

Independent statistical review of the analysis plan before execution, quality control checks on analytical code, and cross-functional review of outputs before inclusion in reports.