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Deep & Rapid Clinical Investigation

Bring us your study data, deliverables, or a clinical question. Receive verified findings, an actionable report, and rich evidence to explore in Nimble Platform.

How It WorksSelf-Investigation

From study data to actionable evidence

AI accelerates preparation and investigation. Experienced professionals verify the findings. Your team receives the report, supporting files, and an interactive environment to explore the evidence.

Ask your own questions in Nimble Platform

Your team can also investigate directly. Ask a study question, use AI agents to perform the analysis, and explore the evidence behind the answer.

01

Ask

  • Start with a question — Define the patients, events, outcomes, or comparison you want to understand.
  • Keep study context close — Work with the data and documents available in your project.
02

Analyze

  • Use AI agents — Prepare analyses, generate programs, and create listings or visualizations for the question.
  • Refine the investigation — Ask follow-up questions, adjust filters, or compare another cohort.
03

Explore & Review

  • Inspect the evidence — Examine results alongside the underlying data, programs, and patient context.
  • Apply expert judgment — Review AI-generated results before relying on them; request Nimble's expert-led service when independent verification is needed.
Explore the Platform →

Study Data Quality Review

What is wrong, inconsistent, incomplete, or unusual in the study data that routine cleaning may have missed?

Look within and across sources

  • Completeness & chronology — Examine missing assessments, inconsistent dates, visit patterns, and eligibility-related gaps.
  • Clinical relationships — Connect adverse events, exposure, laboratory results, and disposition to investigate inconsistencies in context.
  • External-data reconciliation — Compare EDC, laboratory, safety, IRT, and ePRO sources; distinguish source discrepancies from transformation issues.
  • Site & patient patterns — Identify unusual distributions, repeated gaps, and concentrated issues that merit closer review.

Turn findings into follow-up

  • Locate the affected records — Provide subject, site, and record-level evidence with each verified finding.
  • Prioritize action — Explain the issue and recommended follow-up in a report your team can share with its CRO or vendor.
  • Review the coverage — Show completed checks, checks with no findings, and checks that could not be completed.

Read the EXPAND case study to see programmed checks, AI checks and pattern review applied to released clinical study data.

CSR Data & Consistency Review

Does the CSR accurately reflect the statistical outputs and the clinical evidence behind them?

CSR → Tables, Figures & Listings → ADaM → SDTM / Source Data

Check important statements

  • Numbers & denominators — Reconcile population counts, efficacy results, safety summaries, deaths, and discontinuations against supporting outputs.
  • Interpretation & consistency — Review endpoint and subgroup statements, exposure summaries, and consistency with the protocol and SAP.
  • Cross-document alignment — Investigate contradictory numbers, definitions, or conclusions across the agreed documents and outputs.

Trace each discrepancy

  • Follow the evidence chain — Trace selected assertions through tables and analysis datasets to available source evidence.
  • Make correction practical — Identify the relevant statement, conflicting evidence, and proposed follow-up in a reviewer-friendly findings package.
  • Expose traceability gaps — Distinguish a confirmed inconsistency from a statement that cannot be verified with the available material.

Protocol Deviation Review

Have the protocol deviations that occurred been completely and consistently identified?

Protocol requirements → Assessable checks → Potential deviations → Deviation-log reconciliation

Investigate from the protocol

  • Identify assessable requirements — Translate agreed protocol requirements into checks supported by the available data.
  • Detect potential deviations — Examine eligibility, visit windows, required assessments, treatment administration, and other scoped requirements.
  • Reconcile the existing log — Compare independently identified candidates with the sponsor or CRO deviation inventory.

Resolve the differences

  • Investigate omissions & mismatches — Review missing entries, duplicate records, inconsistent classifications, and conflicting supporting data.
  • Apply agreed criteria — Verify candidates and prioritize findings using the study's deviation definitions and review conventions.
  • Document the limits — Make unassessable requirements and missing evidence visible rather than assuming that no deviation occurred.

Independent Biometrics Quality Review

Do the SDTM, ADaM, TFLs, and supporting documents form a correct, coherent, and traceable package?

Programming and analytical outputs reviewed together

Review the analytical chain

  • SDTM & ADaM — Examine mappings, derivations, population flags, and traceability between source and analysis data.
  • SAP & TFLs — Check that endpoint definitions, analysis populations, methods, and reported results align with the agreed analysis plan.
  • Supporting metadata — Review specifications, Define-XML, SDRG, and ADRG for consistency with the delivered datasets.
  • Selected independent reproduction — Reproduce agreed derivations or results and investigate discrepancies.

Go beyond conformance

  • Clinical & analytical meaning — Assess whether the implementation makes sense in study context, alongside standards-validation results.
  • Explain the source of a discrepancy — Distinguish issues in source data, specifications, programming, metadata, and reporting.
  • Enable focused remediation — Deliver verified findings and supporting comparisons that the responsible team can use to correct the package.

Patient & Safety Data Review

Proactively identify and prioritize potential safety concerns through systematic review of adverse events, laboratory results, treatment exposure, and patient histories.

Connect the clinical history

  • Important events — Investigate deaths, serious adverse events, events of special interest, and discontinuations due to adverse events.
  • Treatment & laboratory context — Connect dose interruptions, reductions, exposure, laboratory abnormalities, and concomitant medications.
  • Patient-level evidence — Bring medical history, disposition, protocol deviations, and relevant efficacy data into the same review.

Support focused medical review

  • Move from patterns to patients — Use cohort comparisons and timelines to inspect the cases behind an aggregate signal.
  • Prepare the evidence — Provide relevant listings, visualizations, and verified data findings for the medical-review team.
  • Preserve clinical responsibility — Support the medical monitor's review; medical interpretation and causality decisions remain with the responsible clinicians.

Rapid Clinical Investigation

Bring an important study question. Receive a focused analysis, an evidence-backed answer, and the material needed to explore it further.

A study question investigated through connected evidence

Questions worth investigating

  • Why are dose reductions higher in one cohort?
  • Why does a site report fewer adverse events?
  • What distinguishes patients who discontinue early?
  • Are missing endpoint assessments concentrated at particular sites?
  • Do laboratory abnormalities coincide with treatment interruptions?
  • Which patient characteristics appear associated with a response pattern?

A focused answer, with evidence

  • Build around the question — Agree the population, comparisons, analysis approach, and limitations before interpreting results.
  • Investigate & verify — Combine programming, visual exploration, AI-assisted analysis, and expert assessment of alternative explanations.
  • Deliver a usable answer — Provide a concise report, supporting Excel outputs, and interactive evidence in Nimble Platform; distinguish exploratory associations from established conclusions.

Free focused pilot

What could a deeper review uncover in your study?

Interested, but want to see what this could reveal in your own data? We offer a free, focused AI-assisted review. Together, we agree a useful question and a limited scope, then review the data and share an evidence-linked report. Use the results to assess the depth and relevance before considering a broader engagement.

See what deeper review can do

Watch Beyond Routine Data Checks to explore the review approach and the kinds of clinical data questions it can address.

Watch the webinar →

Explore the EXPAND case study

See discrepancies and contextual review questions identified in released data from a completed Phase III oncology study.

Explore the case study →
Discuss a free pilot →

A conversation about your work

See Something Relevant to Your Study or Team?

Let's discuss your needs, questions, and how Nimble could fit into your clinical workflow.