Clinical Statistics Support
Statistical support for clinical and health research: analysis planning, outcomes, regression, survival methods, repeated measures, and reporting.
Clinical statistics requires alignment between the protocol, outcome definitions, study design, and analysis. Tezyar supports researchers with defensible analysis plans, appropriate models, transparent reporting, and reviewer-driven revisions while keeping clinical interpretation separate from purely statistical significance.
Outcome and Analysis Planning
Primary and secondary outcomes, analysis populations, time points, missing data, and covariates should be specified consistently with the study design.
Common Clinical Modelling Needs
Depending on the design, analyses may include generalized regression, survival methods, repeated-measures or mixed models, diagnostic metrics, and sensitivity analyses.
Missing Data and Sensitivity
Clinical datasets often need explicit handling of incomplete follow-up and missing outcomes, with assumptions and sensitivity analyses reported when relevant.
Clinical Interpretation
Effect estimates, confidence intervals, absolute differences, and clinical importance should be considered alongside statistical evidence.
Frequently Asked Questions
Can you support a retrospective clinical study?
Yes. The analysis must reflect the observational design, available variables, confounding risks, and limits of retrospective inference.
Do you provide medical conclusions?
We support statistical analysis and reporting; clinical conclusions remain the responsibility of the research and clinical team.
Related specialized topics
SPSS analysis support for research projects, theses, and manuscripts: data screening, test selection, modelling, interpretation, and reporting.
R analysis support for reproducible research, statistical modelling, visualization, reporting, and thesis or manuscript workflows.
Structural equation modeling support for CFA, latent variables, path models, fit assessment, model revision, and reporting.
Regression analysis support for linear, logistic, generalized, multivariable, and research-specific modelling workflows.
Sample size and power analysis support for research design, theses, grant proposals, experiments, surveys, and clinical studies.
Survey analysis support for data cleaning, scale reliability, descriptive analysis, group comparisons, regression, factor analysis, and reporting.