My dataset is ready, but I am unsure which model to use.
We review outcomes, predictors, study design and data structure before proposing an analytical strategy.
DataMedLab · Clinical Biostatistics
Biostatistics and advanced data analysis for medical research.
We connect the research question, study design, data structure and clinical context before selecting a statistical approach. The goal is not merely to produce output, but to build a defensible analytical chain.
MD + Biostatistics expertise · within the same analytical workflow
Study planningAdvanced modelingClinically informed interpretation
Network Analysis
Network structure, relationships and stability assessment.
Structural Equation Modeling
Meta-analysis · Forest Plot
When can we help?
You do not need to know the name of the analysis.
We first define the scientific problem from your research question and data structure, then identify the appropriate method.
I need multivariable or advanced analysis.
Regression, survival, mixed models, network analysis, SEM and related approaches can be evaluated.
I received statistical or methodological reviewer comments.
Models, assumptions, effect measures and sensitivity analyses can be reviewed systematically.
My study is still in the planning stage.
Outcomes, variables, sample-size logic and analysis planning can be structured before data collection.
Analytical expertise
The method follows the research question.
We avoid a fixed test-menu approach and instead consider design, assumptions, estimands and clinical interpretability together.
Regression & predictive models
Linear and generalized models, logistic regression, model performance, calibration and clinical prediction.
Survival analysis
Kaplan–Meier methods, Cox models, time-dependent structures and clinically relevant time-to-event outcomes.
Mixed models & SEM
Repeated measures, mixed-effects models, latent variables and structural equation modeling.
Network analysis
Network estimation, centrality, stability, bootstrap assessment and relational structure.
Meta-analysis
Effect sizes, heterogeneity, random-effects models, sensitivity analyses and scientific forest plots.
RWE & observational data
Confounding, propensity methods, cohort structures and comparative analysis strategies.
Why DataMedLab?
Biostatistics that understands the language of medical research.
Clinical outcomes, variable meaning and interpretability are part of statistical decision-making, not an afterthought.
Clinical ContextScientific and clinical meaning considered before modeling
Methodological RigorAssumptions, model choice and sensitivity checks
Advanced MethodsAppropriate modeling beyond standard comparisons
InterpretabilityResults presented with statistical and clinical meaning
Workflow
A clear starting point.
The first step is simply to understand the research question and the current state of the data.
We understand the study.
Objective, design, sample and data structure are reviewed briefly.
We define the analytical path.
Methods, checks and deliverables are specified before the work begins.
We connect results to meaning.
Analysis, visualization and interpretation are developed as one coherent workflow.
Data security
Use anonymized research files.
Research files are transferred through a secure upload area. Direct identifiers must be removed before submission.
Defined scopeNeeds and methods are clarified before project start.
Appropriate methodsAnalysis follows design, data structure and the research question.
Interpretable resultsStatistical output is linked to scientific and clinical meaning.
Remote collaborationEnglish-language project communication is supported worldwide.
DataMedLab · LogoMentis