From the release package documentation
These columns are expected in the outcome object:
- stratumId (integer) (optional) Stratum ID for conditional regression models
- rowId (integer) Row ID is used to link multiple covariates (x) to a single outcome (y)
- y (real) The outcome variable
- time (real) For models that use time (e.g. Poisson or Cox regression) this contains time
(e.g. number of days)
- weights (real) (optional) Non-negative weights to apply to outcome
- censorWeights (real) (optional) Non-negative censoring weights for competing risk model; will be computed if not provided.
These columns are expected in the covariates object:
- stratumId (integer) (optional) Stratum ID for conditional regression models
- rowId (integer) Row ID is used to link multiple covariates (x) to a single outcome (y)
- covariateId (integer) A numeric identifier of a covariate
- covariateValue (real) The value of the specified covariate
The correct way to dealing with timevarying data in a cox model is to split each individual's follow-up period into multiple intervals at each change in their covariate value. Thus a time-varying dataset for cox analysis would have more than [edit] 1 row per person, and the above data spec would require the covariates object to have the same row length as the outcome object. In the case of a cox model with both time-varying and time-invariant variables, all of the time-invariant values would need to be repeated for every interval within participant. A more efficient data structure would allow a time-invariant covariate object which would join to the outcome object on participant id, along with a time-varying covariates object which would link to the outcome on both participant id and time.
From the release package documentation
The correct way to dealing with timevarying data in a cox model is to split each individual's follow-up period into multiple intervals at each change in their covariate value. Thus a time-varying dataset for cox analysis would have more than [edit] 1 row per person, and the above data spec would require the covariates object to have the same row length as the outcome object. In the case of a cox model with both time-varying and time-invariant variables, all of the time-invariant values would need to be repeated for every interval within participant. A more efficient data structure would allow a time-invariant covariate object which would join to the outcome object on participant id, along with a time-varying covariates object which would link to the outcome on both participant id and time.