nonparasccs | R Documentation |

Fits a spline-based non parametric SCCS model where both the exposure related relative incidence and age related relative incidence functions are represented by spline functions; that is, linear combinations of M-splines.

nonparasccs(indiv, astart, aend, aevent, adrug, aedrug, kn1=12, kn2=12, sp1=NULL, sp2=NULL, data)

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

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

`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 related risk period starts. |

`aedrug` |
a vector of ages at which exposure-related risk ends. |

`kn1` |
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. |

`kn2` |
a an integer >= 5 representing the number of interior knots used to define the M-spline basis functions which are related to the exposure specific relative incidence function, usually between 8 and 12 knots is sufficient. The default value is 12. |

`sp1` |
smoothing parameter value for age related relative incidence function. It defaults to "NULL" where the smoothing parameter is obtained automatically using an approximate cross-validation method. The value of "sp1" must be a number greater or equal to 0. |

`sp2` |
smoothing parameter value for exposure related relative incidence function. It defaults to "NULL" where the smoothing paramter is obtained automatically using an approximate cross-validation method. The value of "sp1" must be a number greater or equal to 0. |

`data` |
A data frame containing the input data. |

The smoothing parameters for the age and exposure related relative incidence functions are chosen using a cross-validation method. To visualize the exposure-related relative incidence function, use the plot function.

Relative incidence estimates along with their 95% confidence intervals.

`estimates` |
exposure related relative incidence estimates at each point of time since start of exposure until the maximum difference between the start and end of exposure. |

`timesinceexposure` |
time units since the start of exposure. |

`lci ` |
lower confidence limits of the exposure related relative incidence estimates. |

`uci ` |
upper confidence limits of the exposure related relative incidence estimates. |

Yonas Ghebremichael-Weldeselassie, Heather Whitaker, Paddy Farrington.

Ghebremichael-Weldeselassie, Y., Whitaker, H. J., Farrington, C. P. (2016). Flexible modelling of vaccine effects in self-controlled case series models. Biometrical Journal, 58(3):607-622.

Ghebremichael-Weldeselassie, Y., Whitaker, H. J., Farrington, C. P. (2017). Spline-based self controlled case series method. Statistics in Medicine 33:639-649.

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.

`smoothagesccs`

, `smoothexposccs`

# ITP and MMR data itp.mod <- nonparasccs(indiv=case, astart=sta, aend=end, aevent=itp, adrug=mmr, aedrug=mmr+42, sp1=28000, sp2=1200, data=itpdat) itp.mod # Plot the exposure and age related relative incidence functions plot(itp.mod)

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