NPHNLL: Non Proportional Hazard and Non Log-Linear effect

In flexrsurv: Flexible Relative Survival Analysis

Non Proportional Hazard and Non Log-Linear effect

Description

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

Usage

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 = c(0, max(timevar)), 
       outer.ok = TRUE, 
       Keep.duplicates = TRUE, 
       xdimnames = ":XxXxXXxXxX ", 
       tdimnames = ":TtTtTTtTtT ")

Arguments

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 method=="additive" assumes effects as explained in Remontet et al.(2007), the model method=="multiplicative" assumes effects as explained in Mahboubi et al. (2011).
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 FALSE, which removes duplicate knots with a warning if duplicate interior knots are found.
outer.ok	logical indicating how are managed timevar or x values outside the knots. If FALSE, return NA, if TRUE, return 0 for the corresponding timevar or x values.
xdimnames	string to build dimnames of x bases
tdimnames	string to build dimnames of timevar bases

Details

NPHNLL is based on package orthogonalsplinebasis

References

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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.2656")}

See Also

NPH and NLL.

flexrsurv documentation built on June 7, 2023, 5:09 p.m.