Survival Analysis Consulting
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.
Time-to-Event Analysis
Kaplan-Meier
Cox Regression
Competing Risks
Censored Data
Discuss a Survival Analysis Project
Analyzing outcomes that depend on time?
Survival analysis is required when events occur at different times,
follow-up is incomplete, or not all subjects experience the event of interest.
Practical Survival Analysis for Real-World Data
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.
About Calc Technology Solutions
Based in Wilton, Connecticut, we help organizations improve
operations through custom software, business applications,
automation, analytics, monitoring systems, and engineering solutions.
We serve clients throughout the United States and internationally.
Kaplan-Meier
Kaplan-Meier Estimation
Non-parametric estimation of survival functions with appropriate
handling of censored observations. Used for exploratory analysis,
reporting, and group comparisons over time.
Cox Models
Cox Proportional Hazards Models
Regression modeling of time-to-event data to estimate covariate effects.
Includes assessment of proportional hazards assumptions and appropriate
model diagnostics.
Competing Risks
Competing Risks & Cause-Specific Models
Analysis of situations where multiple event types compete to occur.
Proper estimation of cumulative incidence functions and interpretation
of cause-specific or subdistribution hazards.
Advanced
Advanced Survival Models
Parametric models, time-varying covariates, stratified models,
recurrent events, and extensions for complex study designs and
longitudinal follow-up.
Integration
Integration with Study Design & Data Pipelines
Survival analysis integrated with statistical analysis plans,
data validation workflows, and downstream reporting or regulatory
documentation.
Interpretation
Interpretation & Stakeholder Communication
Clear explanation of hazard ratios, survival curves, assumptions,
and limitations for technical reviewers, decision-makers, and
non-statistical stakeholders.
When Survival Analysis Is Appropriate
- Time-to-event outcomes with censoring
- Unequal follow-up across subjects
- Clinical, health outcomes, or reliability studies
- Operational or policy evaluations over time
- Situations where ordinary regression would be biased
What This Service Is Not
- Introductory coursework or academic tutoring
- Generic statistical summaries without time modeling
- Tool-only support without analytical interpretation
- Methods applied without a clear decision or research goal
Why Clients Choose Us for Survival Analysis
- Deep expertise in time-to-event and censored data methods
- Strong grounding in biostatistics and applied statistical modeling
- Experience supporting research, regulatory, and operational decisions
- Clear focus on assumptions, validity, and interpretability
- Ability to integrate survival analysis into broader analytics workflows