We provide expert survival analysis and time-to-event modeling to support research, regulatory, and operational decisions where outcomes depend on timing, censoring, and incomplete observation.
Survival analysis (also known as time-to-event analysis) is used when the outcome of interest is the timing of an event—such as failure, relapse, recovery, attrition, or progression—and when data include censoring or varying follow-up periods.
We apply survival analysis as a decision-focused statistical tool, supporting clinical research, health outcomes, reliability studies, and operational analyses where standard regression methods are not appropriate.
Non-parametric estimation of survival functions with appropriate handling of censored observations. Used for exploratory analysis, reporting, and group comparisons over time.
Regression modeling of time-to-event data to estimate covariate effects. Includes assessment of proportional hazards assumptions and appropriate model diagnostics.
Analysis of situations where multiple event types compete to occur. Proper estimation of cumulative incidence functions and interpretation of cause-specific or subdistribution hazards.
Parametric models, time-varying covariates, stratified models, recurrent events, and extensions for complex study designs and longitudinal follow-up.
Survival analysis integrated with statistical analysis plans, data validation workflows, and downstream reporting or regulatory documentation.
Clear explanation of hazard ratios, survival curves, assumptions, and limitations for technical reviewers, decision-makers, and non-statistical stakeholders.