createSccsIntervalData(
sccsData,
outcomeId = NULL,
naivePeriod = 0,
firstOutcomeOnly = FALSE,
covariateSettings,
ageSettings = createAgeSettings(includeAge = FALSE),
seasonalitySettings = createSeasonalitySettings(includeSeasonality = FALSE),
minCasesForAgeSeason = 10000,
eventDependentObservation = FALSE
)

## Arguments

sccsData |
An object of type `sccsData` as created using the
`getDbSccsData` function. |

outcomeId |
The outcome to create the era data for. If not specified it is
assumed to be the one outcome for which the data was loaded from
the database. |

naivePeriod |
The number of days at the start of a patient's observation period
that should not be included in the risk calculations. Note that
the naive period can be used to determine current covariate
status right after the naive period, and whether an outcome is
the first one. |

firstOutcomeOnly |
Whether only the first occurrence of an outcome should be
considered. |

covariateSettings |
Either an object of type `covariateSettings` as created
using the `createCovariateSettings` function, or a
list of such objects. |

ageSettings |
An object of type `ageSettings` as created using the
`createAgeSettings` function. |

seasonalitySettings |
An object of type `seasonalitySettings` as created using the
`createSeasonalitySettings` function. |

minCasesForAgeSeason |
Minimum number of cases to use to fit age and season splines. IF
needed (and available), cases that are not exposed will be included.#' |

eventDependentObservation |
Should the extension proposed by Farrington et al. be used to
adjust for event-dependent observation time? |

## Value

An object of type `sccsIntervalData`

.

## Details

This function creates covariates based on the data in the `sccsData`

object, according to the
provided settings. It chops patient time into periods during which all covariates remain constant.
The output details these periods, their durations, and a sparse representation of the covariate
values.

## References

Farrington, C. P., Anaya-Izquierdo, A., Whitaker, H. J., Hocine, M.N., Douglas, I., and Smeeth, L.
(2011). Self-Controlled case series analysis with event-dependent observation periods. Journal of
the American Statistical Association 106 (494), 417-426