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Conduct Kaplan-Meier, Cox regression, and time-to-event analyses
Passed 22 of 23 test cases with the skill, and 21 of 23 without it.
1 fixed21 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
I am preparing the results section of a clinical research manuscript that uses time-to-event analysis. What specific summary metrics, survival statistics, and modeling parameters should I include in my report to meet standard journal reporting guidelines?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends specifying the method used to handle tied event times, such as Efron or Breslow. | Missed | Passed |
| Recommends reporting the total number of events. | Passed | Passed |
| Recommends reporting the total person-time at risk. | Passed | Passed |
The instructions your model reads when the skill loads.
A skill for conducting time-to-event analyses including Kaplan-Meier estimation, log-rank tests, and Cox proportional hazards regression. Covers censoring concepts, assumption checking, and reporting standards for clinical and social science research.
Survival analysis studies the time until an event of interest occurs. Despite the name, the "event" need not be death -- it can be any well-defined transition:
Medical: Time to disease recurrence, death, or recovery
Engineering: Time to equipment failure
Social: Time to job termination, divorce, or graduation
Business: Time to customer churn or first purchase
Ecology: Time to species extinction in a habitat
Right censoring (most common):
The event has not occurred by the end of the study period.
Example: Patient is still alive at study end.
The survival time is "at least T" -- we know T but not the true event time.
Left censoring:
The event occurred before the observation period began.
Example: HIV infection detected, but seroconversion happened before testing.
Interval censoring:
The event occurred between two observation times.
Example: A patient tests negative at visit 3 and positive at visit 4.
test cases passed, vs 21 / 23 without
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