# plot.ddsurvcurve

##### Create and plot survival curves

The function creates a predicted survival curve for a new observation using
a estimated `ddhazard`

model from `ddhazard`

. The predicted
curve is based on the predicted mean path of the state vector. Thus, the
survival curve will not be a "mean" curve due to the non-linear relation between
the probability of an event and the state vector.

##### Usage

```
# S3 method for ddsurvcurve
plot(x, y, xlab = "Time", ylab = "Survival",
ylim, xaxs = "i", yaxs = "i", ...)
```# S3 method for ddsurvcurve
lines(x, col = "Black", lty = 1,
lwd = par()$lwd, ...)

ddsurvcurve(object, new_data, tstart = "", tstop = "")

##### Arguments

- x
a

`ddsurvcurve`

object.- y
not used.

- xlab
`xlab`

passed to`plot`

.- ylab
`ylab`

passed to`plot`

.- ylim
`ylim`

passed to`plot`

.- xaxs
`xaxs`

passed to`plot`

.- yaxs
`yaxs`

passed to`plot`

.- ...
not used.

- col
`col`

passed to`lines`

.- lty
`lty`

passed to`lines`

.- lwd
`lwd`

passed to`lines`

.- object
a

`ddhazard`

object.- new_data
a

`data.frame`

with the new data for the observation who the survival curve should be for. It can have more rows if`tstart`

and`tstop`

is supplied. The rows need to be consecutive and non-overlapping time intervals.- tstart
name of the start time column in

`new_data`

. It must be on the same time scale as the`tstart`

used in the`Surv(tstart, tstop, event)`

in the`formula`

passed to`ddhazard`

.- tstop
same as

`tstart`

for the stop argument.

##### Value

`ddsurvcurve`

returns an object of class `ddsurvcurve`

. It elements are the predicted
discrete survival curve, time points for the survival curve, point of the first
time period, the call, the discrete probabilities of an event in each interval
conditional on survival up to that point, and the name of the distribution
family. It should be seen as a plug-in estimate.

##### Methods (by generic)

`plot`

: method for plotting survival curve.`lines`

: Method for adding survival curve to a plot.

##### plot.ddsurvcurve

Returns the same as `lines.ddsurvcurve`

.

##### lines.ddsurvcurve

Either returns the objects used in the call to `segments`

for discrete
time hazard models, or the time points and survival function used to draw
the survival curve.

##### See Also

`ddhazard`

, and `predict.ddhazard`

.

##### Examples

```
# NOT RUN {
#####
# example with continuous time model
# setup data set. See `vignette("timedep", "survival")`
library(dynamichazard)
temp <- subset(pbc, id <= 312, select=c(id:sex, stage))
pbc2 <- tmerge(temp, temp, id=id, death = event(time, status))
pbc2 <- tmerge(pbc2, pbcseq, id = id, bili = tdc(day, bili))
# fit model
f1 <- ddhazard(
Surv(tstart, tstop, death == 2) ~ ddFixed(log(bili)), pbc2, id = pbc2$id,
max_T = 3600, Q_0 = 1, Q = 1e-2, by = 100, model = "exponential",
control = ddhazard_control(method = "EKF", eps = 1e-4, n_max = 1000,
fixed_terms_method = "M_step"))
# predict with default which is all covariates set to zero
ddcurve <- ddsurvcurve(f1)
par(mar = c(4.5, 4, .5, .5))
plot(ddcurve, col = "DarkBlue", lwd = 2)
# compare with cox model
f2 <- coxph(Surv(tstart, tstop, death == 2) ~ log(bili), data = pbc2)
nw <- data.frame(bili = 1, tstart = 0, tstop = 3000)
lines(survfit(f2, newdata = nw))
# same as above but with bili = 3
nw <- data.frame(bili = 3)
lines(ddsurvcurve(f1, new_data = nw), col = "DarkBlue")
lines(survfit(f2, newdata = nw))
# change to time-varying slope
f3 <- ddhazard(
Surv(tstart, tstop, death == 2) ~ log(bili), pbc2, id = pbc2$id,
max_T = 3600, Q_0 = diag(1, 2), Q = diag(1e-2, 2), by = 100, model = "exponential",
control = ddhazard_control(method = "EKF", eps = 1e-4, n_max = 1000))
# example with time-varying coefficient
nw <- data.frame(
bili = c(2.1, 1.9, 3.3, 3.9, 3.8, 3.6, 4, 4.9, 4.2, 5.7, 10.2),
tstart = c(0L, 225L, 407L, 750L, 1122L, 1479L, 1849L, 2193L, 2564L, 2913L,
3284L),
tstop = c(225L, 407L, 750L, 1122L, 1479L, 1849L, 2193L, 2564L, 2913L,
3284L, 3600L))
ddcurve <- ddsurvcurve(f3, new_data = nw, tstart = "tstart", tstop = "tstop")
lines(ddcurve, "darkorange", lwd = 2)
# can condition on survival up to some time
ddcurve <- ddsurvcurve(f3, new_data = nw[-(1:5), ], tstart = "tstart",
tstop = "tstop")
lines(ddcurve, lty = 2, lwd = 2)
#####
# example with discrete time model
# head-and-neck cancer study data. See Efron, B. (1988) doi:10.2307/2288857
is_censored <- c(
6, 27, 34, 36, 42, 46, 48:51, 51 + c(15, 30:28, 33, 35:37, 39, 40, 42:45))
head_neck_cancer <- data.frame(
id = 1:96,
stop = c(
1, 2, 2, rep(3, 6), 4, 4, rep(5, 8),
rep(6, 7), 7, 8, 8, 8, 9, 9, 10, 10, 10, 11, 14, 14, 14, 15, 18, 18, 20,
20, 37, 37, 38, 41, 45, 47, 47,
2, 2, 3, rep(4, 4), rep(5, 5), rep(6, 5),
7, 7, 7, 9, 10, 11, 12, 15, 16, 18, 18, 18, 21,
21, 24, 25, 27, 36, 41, 44, 52, 54, 59, 59, 63, 67, 71, 76),
event = !(1:96 %in% is_censored),
group = factor(c(rep(1, 45 + 6), rep(2, 45))))
# fit model
h1 <- ddhazard(
Surv(stop, event) ~ group, head_neck_cancer, by = 1, max_T = 45,
Q_0 = diag(2^2, 2), Q = diag(.01^2, 2), control = ddhazard_control(
method = "GMA", eps = 1e-4, n_max = 200))
# plot predicted survival curve. Notice the steps since the model is discrete
nw <- data.frame(group = factor(1, levels = 1:2), tstart = 0, tstop = 30)
ddcurve <- ddsurvcurve(h1, new_data = nw, tstart = "tstart",
tstop = "tstop")
plot(ddcurve, col = "Darkblue")
nw$group <- factor(2, levels = 1:2)
ddcurve <- ddsurvcurve(h1, new_data = nw, tstart = "tstart",
tstop = "tstop")
lines(ddcurve, col = "DarkOrange")
# compare with KM
lines(survfit(Surv(stop, event) ~ 1, head_neck_cancer, subset = group == 1),
col = "DarkBlue")
lines(survfit(Surv(stop, event) ~ 1, head_neck_cancer, subset = group == 2),
col = "DarkOrange")
# }
```

*Documentation reproduced from package dynamichazard, version 0.6.5, License: GPL-2*