smoothagesccs | R Documentation |

Fits a semiparametric SCCS model with smooth age effect, where the age related relative incidence function is represented by spline function; that is, linear combinations of M-splines. The exposure related relative incidence function is represented by step functions. One exposure group can be included.

smoothagesccs(indiv, astart, aend, aevent, adrug, aedrug, expogrp = 0, washout = NULL, kn=12, sp = NULL, data)

`indiv` |
a vector of individual identifiers of cases. |

`astart` |
a vector of ages at which observation periods start. |

`aend` |
a vector of ages at end of observation periods. |

`aevent` |
a vector of ages at event, an individual can experience multiple events. |

`adrug` |
a vector of ages at which exposure starts, only a single exposure type can be included. |

`aedrug` |
a vector of ages at which the exposure-related risk periods end. |

`expogrp` |
a vector of days to the start of exposure-related risk, counted from |

`washout` |
a vector of days to start of washout periods counted from |

`kn` |
an integer >=5 representing the number of interior knots used to define the M-spline basis functions which are related to the age specific relative incidence function, usually between 8 and 12 knots is sufficient. It defaults to 12 knots. |

`sp` |
smoothing parameter value. It defaults to "auto" where the smoothing paramter is obtained automatically using a cross-validation method. The value of "sp" must be a number greater or equal to 0. |

`data` |
a data frame containing the input data. The data are assembled one line per event. |

The standard SCCS represents the age and exposure effects by piecewise constant step functions, however mis-specification of age group cut points might lead to biased estimates of the exposure related relative incidences. The semiparametric SCCS model, `semisccs`

, has numerical challenges when the number of cases is large. This splined-based semiparametric SCCS model with smooth age effect avoids these limitations of the standard and semiparametric SCCS models. The smoothing parameter for the age-related relative incidence function is chosen by an approximate cross-validation method. The method is outlined in Ghebremichael-Weldeselassie et al (2014).

Relative incidence estimates along with their 95% confidence limits.

`coef` |
log of the exposure related relative incidence estimates. |

`se` |
standard errors of the log of exposure related relative incidence estimates. |

`age` |
age related relative incidences at each day between the minimum age at start of observation and maximum age at end of observation periods. |

`ageaxis` |
sequence of ages between the minimum age at start of observations and maximum age at end of observation periods corresponding to the age related relative incidences. |

`smoothingpara` |
smoothing parameter chosen by maximizing an approximate cross-validation score or given as an argument in the function |

`cv` |
cross-validation score |

Yonas Ghebremichael-Weldeselassie, Heather Whitaker, Paddy Farrington.

Ghebremichael-Weldeselassie, Y., Whitaker, H. J., Farrington, C. P. (2015). Self-controlled case series method with smooth age effect. Statistics in Medicine, 33(4), 639-649.

Farrington, P., Whitaker, H., and Ghebremichael-Weldeselassie, Y. (2018). Self-controlled Case Series Studies: A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press.

`smoothexposccs`

# Fit the SCCS model with smooth age effect to the itp data and plot age effect. itp.mod <- smoothagesccs(indiv=case, astart=sta,aend=end, aevent=itp, adrug=mmr, aedrug=mmr+42, expogrp=c(0,15,29), sp=2800, data=itpdat) itp.mod plot(itp.mod)

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