Namtso Solutions CORP.

Data analysis & real-world evidence

Data analysis and reports you can use.

We help research and business teams analyze data, interpret results and turn findings into reports for decision-makers.

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Printed charts and an analyst’s notebook on a desk
Illustrative extraction template
Population
Eligibility, setting, baseline characteristics
Study design
Data source, cohort definition, follow-up
Outcomes
Endpoint definitions, estimates, limitations

Illustrative issue

How we read a company disclosure

A fictional example of how the Namtso brief separates what a company reports from what the evidence can support.

What the company reported

A medical device maker reports fewer readmissions among patients discharged with a remote monitoring device than among patients receiving usual care.

What the evidence supports

The finding describes an association in an observational comparison. It may justify further study. A causal interpretation depends on how the groups were selected and how confounding was addressed.

What we would verify

Check the cohort definitions, baseline differences, follow-up window, adjusted effect estimate and uncertainty. In a live issue, cite the original disclosure and any public study protocol or results.

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Analysis, reporting and research

Data analysis

Check data quality, summarize trends and compare results across groups or periods. Receive charts and tables with a clear explanation of the methods.

Data reporting

Get recurring reports with key metrics, notable changes, charts and source notes. Choose two or five reports per week based on how often you need updates.

Evidence research

Find relevant studies, extract outcomes into an evidence table and summarize findings and limitations with source references.

Research examples

Compare outcomes in routine care

Explore a synthetic heart failure study and see how baseline differences and analysis choices affect the estimated readmission risk.

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Evaluate data before licensing

Start with the research question. Map the required population, exposures, endpoints and follow-up to each candidate dataset. Use the gaps to decide what needs validation before purchase.

Review the project designs

Assess an external comparator

Check whether a routine-care cohort can reproduce the trial’s eligibility, time zero and endpoint definitions before attempting an effect estimate.

See the feasibility scope

Prepare a project brief

Capture the question, deliverable and timing. Use study-level details and leave out patient identifiers.

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