Generate the design matrix of spline basis for both non log-linear and non proportional effect.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
NPHNLL(x,
timevar,
model = c("additive", "multiplicative"),
Spline = c("b-spline", "tp-spline", "tpi-spline"),
Knots = NULL,
Degree = 3,
Intercept = FALSE,
Boundary.knots = range(x),
Knots.t = NULL,
Degree.t = 3,
Intercept.t = (model == "multiplicative"),
Boundary.knots.t = range(timevar),
outer.ok = TRUE,
Keep.duplicates = TRUE,
xdimnames = ":XxXxXXxXxX ",
tdimnames = ":TtTtTTtTtT ")
``` |

`x` |
the predictor variable. |

`timevar` |
the time variable. |

`model` |
character string specifying the type of model for both non-proportionnal and non linear effects.
The model |

`Spline` |
a character string specifying the type of spline basis. "b-spline" for B-spline basis, "tp-spline" for truncated power basis and "tpi-spline" for monotone (increasing) truncated power basis. |

`Knots` |
the internal breakpoints that define the spline used to estimate the NLL part of effect. By default there are none. |

`Degree` |
degree of splines of variable which are considered. |

`Intercept` |
a logical value indicating whether intercept/first basis of spline should be considered. |

`Boundary.knots` |
range of variable which is analysed. |

`Knots.t` |
the internal breakpoints that define the spline used to estimate the NPH part of effect. By default there are none. |

`Degree.t` |
degree of splines of time variable which are considered. |

`Intercept.t` |
a logical value indicating whether intercept/first basis of spline should be considered. |

`Boundary.knots.t` |
range of time period which is analysed. |

`Keep.duplicates` |
Should duplicate interior knots be kept or removed. Defaults is |

`outer.ok` |
logical indicating how are managed |

`xdimnames` |
string to build dimnames of |

`tdimnames` |
string to build dimnames of |

`NPHNLL`

is based on package `orthogonalsplinebasis`

Mahboubi, A., M. Abrahamowicz, et al. (2011). "Flexible modeling of the effects of continuous prognostic factors in relative survival." Stat Med 30(12): 1351-1365. doi: 10.1002/sim.4208

Remontet, L., N. Bossard, et al. (2007). "An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies." Stat Med 26(10): 2214-2228. doi: 10.1002/sim.2656

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