Survsplinek: Royston/Parmar spline survival distribution functions with...

In chjackson/flexsurv-dev: Flexible Parametric Survival and Multi-State Models

Royston/Parmar spline survival distribution functions with one argument per parameter

Description

Probability density, distribution, quantile, random generation, hazard, cumulative hazard, mean and restricted mean functions for the Royston/Parmar spline model, with one argument per parameter. For the equivalent functions with all parameters collected together in a single argument, see Survspline.

Usage

mean_survspline0(
  gamma0,
  gamma1,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

mean_survspline1(
  gamma0,
  gamma1,
  gamma2,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

mean_survspline2(
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

mean_survspline3(
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

mean_survspline4(
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

mean_survspline5(
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

mean_survspline6(
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

mean_survspline7(
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  gamma8,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

rmst_survspline0(
  t,
  gamma0,
  gamma1,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  start = 0
)

rmst_survspline1(
  t,
  gamma0,
  gamma1,
  gamma2,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  start = 0
)

rmst_survspline2(
  t,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  start = 0
)

rmst_survspline3(
  t,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  start = 0
)

rmst_survspline4(
  t,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  start = 0
)

rmst_survspline5(
  t,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  start = 0
)

rmst_survspline6(
  t,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  start = 0
)

rmst_survspline7(
  t,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  gamma8,
  knots = c(-10, 10),
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  start = 0
)

dsurvspline0(
  x,
  gamma0,
  gamma1,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  log = FALSE
)

dsurvspline1(
  x,
  gamma0,
  gamma1,
  gamma2,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  log = FALSE
)

dsurvspline2(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  log = FALSE
)

dsurvspline3(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  log = FALSE
)

dsurvspline4(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  log = FALSE
)

dsurvspline5(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  log = FALSE
)

dsurvspline6(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  log = FALSE
)

dsurvspline7(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  gamma8,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  log = FALSE
)

psurvspline0(
  q,
  gamma0,
  gamma1,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

psurvspline1(
  q,
  gamma0,
  gamma1,
  gamma2,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

psurvspline2(
  q,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

psurvspline3(
  q,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

psurvspline4(
  q,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

psurvspline5(
  q,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

psurvspline6(
  q,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

psurvspline7(
  q,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  gamma8,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

qsurvspline0(
  p,
  gamma0,
  gamma1,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

qsurvspline1(
  p,
  gamma0,
  gamma1,
  gamma2,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

qsurvspline2(
  p,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

qsurvspline3(
  p,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

qsurvspline4(
  p,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

qsurvspline5(
  p,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

qsurvspline6(
  p,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

qsurvspline7(
  p,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  gamma8,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp",
  lower.tail = TRUE,
  log.p = FALSE
)

rsurvspline0(
  n,
  gamma0,
  gamma1,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

rsurvspline1(
  n,
  gamma0,
  gamma1,
  gamma2,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

rsurvspline2(
  n,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

rsurvspline3(
  n,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

rsurvspline4(
  n,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

rsurvspline5(
  n,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

rsurvspline6(
  n,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

rsurvspline7(
  n,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  gamma8,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

hsurvspline0(
  x,
  gamma0,
  gamma1,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

hsurvspline1(
  x,
  gamma0,
  gamma1,
  gamma2,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

hsurvspline2(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

hsurvspline3(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

hsurvspline4(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

hsurvspline5(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

hsurvspline6(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

hsurvspline7(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  gamma8,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

Hsurvspline0(
  x,
  gamma0,
  gamma1,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

Hsurvspline1(
  x,
  gamma0,
  gamma1,
  gamma2,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

Hsurvspline2(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

Hsurvspline3(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

Hsurvspline4(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

Hsurvspline5(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

Hsurvspline6(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

Hsurvspline7(
  x,
  gamma0,
  gamma1,
  gamma2,
  gamma3,
  gamma4,
  gamma5,
  gamma6,
  gamma7,
  gamma8,
  knots,
  scale = "hazard",
  timescale = "log",
  spline = "rp"
)

Arguments

gamma0, gamma1, gamma2, gamma3, gamma4, gamma5, gamma6, gamma7, gamma8	Parameters describing the baseline spline function, as described in flexsurvspline.
knots	Locations of knots on the axis of log time, supplied in increasing order. Unlike in flexsurvspline, these include the two boundary knots. If there are no additional knots, the boundary locations are not used. If there are one or more additional knots, the boundary knots should be at or beyond the minimum and maximum values of the log times. In flexsurvspline these are exactly at the minimum and maximum values. This may in principle be supplied as a matrix, in the same way as for gamma, but in most applications the knots will be fixed.
scale	"hazard", "odds", or "normal", as described in flexsurvspline. With the default of no knots in addition to the boundaries, this model reduces to the Weibull, log-logistic and log-normal respectively. The scale must be common to all times.
timescale	"log" or "identity" as described in flexsurvspline.
spline	"rp" to use the natural cubic spline basis described in Royston and Parmar. "splines2ns" to use the alternative natural cubic spline basis from the splines2 package (Wang and Yan 2021), which may be better behaved due to the basis being orthogonal.
start	Optional left-truncation time or times. The returned restricted mean survival will be conditioned on survival up to this time.
x, q, t	Vector of times.
log, log.p	Return log density or probability.
lower.tail	logical; if TRUE (default), probabilities are P(X \le x), otherwise, P(X > x).
p	Vector of probabilities.
n	Number of random numbers to simulate.

Details

These functions go up to 7 spline knots, or 9 parameters.

Author(s)

Christopher Jackson <[e-mail]>

chjackson/flexsurv-dev documentation built on April 23, 2024, 10:57 a.m.