Kaplan-Meier curves, Nelson-Aalen hazards, life tables, and Cox proportional hazards models
Survival analysis is a statistical method used to analyze the time until an event occurs. In actuarial science, this "event" could be death, policy lapse, claim occurrence, or any other time-dependent outcome you want to study.
For example, if you're analyzing life insurance data, survival analysis helps you understand:
What it is: A step-by-step graph showing the probability of survival over time.
Why it's useful: Easily visualize how many people survive at each time point. The curve drops each time someone experiences the event (e.g., death). You can compare multiple groups on the same graph to see which has better survival rates.
What it is: The cumulative hazard function—essentially the accumulated risk over time.
Why it's useful: While Kaplan-Meier shows survival probability, Nelson-Aalen shows accumulated risk. It's particularly useful for understanding how risk builds up over time and for comparing hazard rates between groups.
What it is: A detailed table showing survival statistics at each time interval.
Why it's useful: Provides exact numbers for survival probability, number at risk, number of events, and hazard rates at each time point. Essential for actuarial calculations and regulatory reporting.
What it is: A regression model that identifies which factors affect survival time.
Why it's useful: Answers questions like "Does smoking increase mortality risk?" or "How much does age affect survival?" Provides hazard ratios that quantify the impact of each factor.
To perform survival analysis, your dataset should contain:
The duration from start to event (or end of observation). Examples: age at death, years since policy issue, months until claim.
A column indicating whether the event occurred (1) or the observation was censored (0). Censored means we stopped observing before the event happened.
Variables to compare groups (e.g., gender, smoking status, policy type). ADaaS will create separate survival curves for each group.
Analyze mortality patterns to set appropriate premiums. Compare survival rates across different risk classes and age groups.
Study how long customers keep their policies before lapsing. Identify factors that lead to early termination.
Analyze time until first claim for health or disability insurance. Understand claim patterns and risk factors.
Estimate retirement duration and longevity for pension fund planning. Calculate expected benefit payments.
Automatic Detection: Upload your data and ADaaS automatically identifies time and status columns using AI.
One-Click Analysis: Generate Kaplan-Meier curves, life tables, and Cox models with a single click—no coding required.
Interactive Visualizations: Explore survival curves with hover tooltips, zoom capabilities, and group comparisons.
Export-Ready Reports: Generate professional PDF/DOCX reports with all survival analysis results for stakeholders.
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