Laboratory Data Analytics

The reliability of laboratory tests depends not only on measurement processes but also on evaluating results with appropriate statistical methods. Diagnostic performance, cut-offs, agreement between methods, and reference intervals play a key role in the clinical interpretation of laboratory data.

At Data4Care we provide expert support to clinical laboratories, researchers, and healthcare institutions on the statistical analysis of laboratory data. We combine classical statistical methods with advanced analytics to deliver solutions suited to research and practice needs.

What We Cover

Diagnostic Performance Analyses

ROC curves and AUC analysis
Sensitivity and specificity calculations
Positive and negative predictive values
Evaluation of diagnostic cut-offs
Comparison of ROC curves
DeLong test and appropriate comparison methods
Performance analysis of diagnostic tests and biomarkers

Laboratory Method Comparisons

Bland–Altman analysis
Correlation- and regression-based method comparisons
Deming and Passing–Bablok regression
Assessment of systematic and proportional differences between measurement methods
Method agreement and analytical performance evaluation

Reference Intervals

Verification of existing reference intervals
Establishment of laboratory-specific reference intervals
Direct and indirect reference interval methods
Subgroup assessments by factors such as age and sex
Review of outliers and data distributions
Statistical reporting of reference interval results

Advanced Laboratory Data Analytics

Analysis of large-scale laboratory datasets
Comparative evaluation of biomarkers
Prediction models based on laboratory data
Comparisons of statistical and machine learning approaches

Who Is It For?

Medical biochemistry and medical microbiology laboratories, hospitals, clinical laboratory specialists, researchers developing diagnostic tests, and health technology organizations working with laboratory data.

How We Work

We review the laboratory’s available data, test characteristics, and analysis aim. Suitable statistical methods are selected, and data checks and analyses are performed.

Results are reported with attention to the assumptions and limitations of the methods used. Where needed, different methods are compared and evaluations are prepared based on the laboratory’s own data.

A report including analysis results, figures, statistical assessments, and methodological explanations can be provided at the end of the project.

Reference Interval Analytics with EazyRI

EazyRI, developed by Data4Care, is a web-based software solution that supports reference interval analyses based on laboratory data.

We can also offer project approaches that combine software-based analysis with expert statistical evaluation for reference interval studies.

Want to use your laboratory data more effectively?