REPOSITORY CODING · MEDIUM
Include subjects failing or censoring at a time in that time risk set
Include subjects failing or censoring at a time in that time risk set. Process tied failures before removing censors. The complete application includes native models, services, repository projections, a working preview and visible acceptance cases. Preserve the other documented invariants and run bash test/run.sh.
survival-analysisKaplan Meier survival laboratory
Work through a real codebase.
Choose an environment, investigate the existing implementation and use the tests to check your change. Sign in to open a workspace; execution and Ono usage follow your plan’s allowances.
Task requirements and environments
PySpark · 55 minute session
- Include subjects failing or censoring at a time in that time risk set. Process tied failures before removing censors.
- Respect delayed entry in every risk set. Subjects who enter after an event are not at risk for it.
- Use Greenwood variance S squared times sum d divided by n times n minus d, with terminal extinction variance zero.
- The Kaplan-Meier median is the first time survival is at most one half, and survival updates use the full risk-set fraction.
- Observe events at the inclusive horizon and preserve the censor count in each output point. Future exits become administrative censoring.
- At most forty distinct subjects and nonnegative integer planning times. Cohorts remain separate. Reports round survival and variance to six decimal places; no confidence model is implied beyond the documented estimator. Spark performs risk-set joins and event grouping.