# Survspline: Royston/Parmar spline survival distribution In flexsurv: Flexible parametric survival models

## Description

Probability density and distribution function for the Royston/Parmar spline model.

## Usage

 ```1 2 3 4``` ```dsurvspline(x, gamma, beta=0, X=0, knots=c(-10,10), scale="hazard", offset=0) psurvspline(q, gamma, beta=0, X=0, knots=c(-10,10), scale="hazard", offset=0) hsurvspline(x, gamma, beta=0, X=0, knots=c(-10,10), scale="hazard", offset=0) Hsurvspline(x, gamma, beta=0, X=0, knots=c(-10,10), scale="hazard", offset=0) ```

## Arguments

 `x,q` Vector of times. `gamma` Vector of parameters describing the baseline spline function, as described in `flexsurvspline`. `beta` Vector of covariate effects. `X` Matrix of covariate values. `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. `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. `offset` An extra constant to add to the linear predictor eta.

## Value

`dsurvspline` gives the density, `psurvspline` gives the distribution function, `hsurvspline` gives the hazard and `Hsurvspline` gives the cumulative hazard, as described in `flexsurvspline`.

## Author(s)

Christopher Jackson <[email protected]>

## References

Royston, P. and Parmar, M. (2002). Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine 21(1):2175-2197.

`flexsurvspline`.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```## reduces to the weibull regscale <- 0.786; cf <- 1.82 a <- 1/regscale; b <- exp(cf) dweibull(1, shape=a, scale=b) dsurvspline(1, gamma=c(log(1 / b^a), a)) # should be the same ## reduces to the log-normal meanlog <- 1.52; sdlog <- 1.11 dlnorm(1, meanlog, sdlog) dsurvspline(1, gamma = c(-meanlog/sdlog, 1/sdlog), scale="normal") # should be the same ```

### Example output

```Loading required package: survival
[1] 0.1137858
[1] 0.1137858
[1] 0.1407338
[1] 0.1407338
```

flexsurv documentation built on May 31, 2017, 4:50 a.m.