survplot {Design} | R Documentation

## Plot Survival Curves and Hazard Functions

### Description

Plot estimated survival curves, and for parametric survival models, plot hazard functions. There is an option to print the number of subjects at risk at the start of each time interval. Curves are automatically labeled at the points of maximum separation (using the `labcurve` function), and there are many other options for labeling that can be specified with the `label.curves` parameter. For example, different plotting symbols can be placed at constant x-increments and a legend linking the symbols with category labels can automatically positioned on the most empty portion of the plot.

### Usage

    survplot(fit, ...)
    ## S3 method for class 'Design':
    survplot(fit, ..., xlim,
             ylim=if(loglog) c(-5, 1.5) else if
                     (what == "survival" & missing(fun)) c(0, 1),
             xlab, ylab, time.inc,
             what=c("survival","hazard"),
             type=c("tsiatis","kaplan-meier"),
             conf.type=c("log-log","log","plain","none"),
             conf.int=FALSE, conf=c("bars","bands"),
             add=FALSE, label.curves=TRUE,
             abbrev.label=FALSE, lty, lwd=par("lwd"), col=1,
             adj.subtitle, loglog=FALSE, fun,
             n.risk=FALSE, logt=FALSE, dots=FALSE, dotsize=.003,
             grid=FALSE, srt.n.risk=0, sep.n.risk=0.056, adj.n.risk=1,
             y.n.risk, cex.n.risk=.6, pr=FALSE)
    ## S3 method for class 'survfit':
    survplot(fit, xlim,
             ylim, xlab, ylab, time.inc,
             conf=c("bars","bands","none"), add=FALSE,
             label.curves=TRUE, abbrev.label=FALSE,
             lty,lwd=par('lwd'),col=1,
             loglog=FALSE,fun,n.risk=FALSE,logt=FALSE,
             dots=FALSE,dotsize=.003,
             grid=FALSE,
             srt.n.risk=0,sep.n.risk=.056,adj.n.risk=1,
             y.n.risk,cex.n.risk=.6, pr=FALSE, ...)

### Arguments

`fit` | result of fit (`cph`, `psm`, `survfit`, `survest.psm`)
`...` | list of factors with names used in model. For fits from `survfit`, these arguments do not appear - all strata are plotted. Otherwise the first factor listed is the factor used to determine different survival curves. Any other factors are used to specify single constants to be adjusted to, when defaults given to fitting routine (through `limits`) are not used. The value given to factors is the original coding of data given to fit, except that for categorical or strata factors the text string levels may be specified. The form of values given to the first factor are `NA` (use default range or list of all values if variable is discrete), `"text"` if factor is categorical, `c(value1, value2, ...)`, or a function which returns a vector, such as `seq(low,high,by=increment)`. `NA` may be specified only for the first factor. In this case the `Low effect`, `Adjust to`, and `High effect` values will be used from `datadist` if the variable is continuous. For variables not defined to `datadist`, you must specify non-missing constant settings (or a vector of settings for the one displayed variable). Note that since `survfit` objects do not use the variable list in `...`, you can specify any extra arguments to `labcurve` by adding them at the end of the list of arguments.
`xlim` | a vector of two numbers specifiying the x-axis range for follow-up time. Default is `(0,maxtime)` where `maxtime` was the `pretty()`d version of the maximum follow-up time in any stratum, stored in `fit$maxtime`. If `logt=TRUE`, default is `(1, log(maxtime))`.
`ylim` | y-axis limits. Default is `c(0,1)` for survival, and `c(-5,1.5)` if `loglog=TRUE`. If `fun` or `loglog=TRUE` are given and `ylim` is not, the limits will be computed from the data. For `what="hazard"`, default limits are computed from the first hazard function plotted.
`xlab` | x-axis label. Default is `units` attribute of failure time variable given to `Surv`.
`ylab` | y-axis label. Default is `"Survival Probability"` or `"log(-log Survival Probability)"`. If `fun` is given, the default is `""`. For `what="hazard"`, the default is `"Hazard Function"`.
`time.inc` | time increment for labeling the x-axis and printing numbers at risk. If not specified, the value of `time.inc` stored with the model fit will be used.
`type` | specifies type of estimates, `"tsiatis"` (the default) or `"kaplan-meier"`. `"tsiatis"` here corresponds to the Breslow estimator. This is ignored if survival estimates stored with `surv=TRUE` are being used. For fits from `survfit`, this argument is also ignored, since it is specified as an argument to `survfit`.
`conf.type` | specifies the basis for confidence limits. If estimates stored with `surv=TRUE` are being used, always uses `"log-log"`, the default. This argument is ignored for fits from `survfit`.
`conf.int` | Default is `FALSE`. Specify e.g. `.95` to plot 0.95 confidence bands. For fits from parametric survival models, or Cox models with `x=TRUE` and `y=TRUE` specified to the fit, the exact asymptotic formulas will be used to compute standard errors, and confidence limits are based on `log(-log S(t))`. If `x=TRUE` and `y=TRUE` were not specified to `cph` but `surv=TRUE` was, the standard errors stored for the underlying survival curve(s) will be used. These agree with the former if predictions are requested at the mean value of X beta or if there are only stratification factors in the model. This argument is ignored for fits from `survfit`, which must have previously specified confidence interval specifications.
`conf` | `"bars"` for confidence bars at each `time.inc` time point. If the fit was from `cph(..., surv=TRUE)`, the `time.inc` used will be that stored with the fit. Use `conf="bands"` for bands using standard errors at each failure time. For `survfit` objects only, `conf` may also be `"none"`, indicating that confidence interval information stored with the `survfit` result should be ignored.
`what` | defaults to `"survival"` to plot survival estimates. Set to `"hazard"` or an abbreviation to plot the hazard function (for `psm` fits only). Confidence intervals are not available for `what="hazard"`.
`add` | set to `TRUE` to add curves to an existing plot.
`label.curves` | default is `TRUE` to use `labcurve` to label curves where they are farthest apart. Set `label.curves` to a `list` to specify options to `labcurve`, e.g., `label.curves=list(method="arrow", cex=.8)`. These option names may be abbreviated in the usual way arguments are abbreviated. Use for example `label.curves=list(keys=1:5)` to draw symbols (as in `pch=1:5` - see `points`) on the curves and automatically position a legend in the most empty part of the plot. Set `label.curves=FALSE` to suppress drawing curve labels. The `col`, `lty`, `lwd`, and `type` parameters are automatically passed to `labcurve`, although you can override them here. To distinguish curves by line types and still have `labcurve` construct a legend, use for example `label.curves=list(keys="lines")`. The negative value for the plotting symbol will suppress a plotting symbol from being drawn either on the curves or in the legend.
`abbrev.label` | set to `TRUE` to `abbreviate()` curve labels that are plotted
`lty` | vector of line types to use for different factor levels. Default is `c(1,3,4,5,6,7,...)`.
`lwd` | vector of line widths to use for different factor levels. Default is current `par` setting for `lwd`.
`col` | color for curve, default is `1`. Specify a vector to assign different colors to different curves.
`adj.subtitle` | set to `FALSE` to suppress plotting subtitle with levels of adjustment factors not plotted. Defaults to `TRUE` if there are 4 or fewer adjustment factors. This argument is ignored for `survfit`.
`loglog` | set to `TRUE` to plot `log(-log Survival)` instead of `Survival`
`fun` | specifies any function to translate estimates and confidence limits before plotting
`logt` | set to `TRUE` to plot `log(t)` instead of `t` on the x-axis
`n.risk` | set to `TRUE` to add number of subjects at risk for each curve, using the `surv.summary` created by `cph` or using the failure times used in fitting the model if `y=TRUE` was specified to the fit or if the fit was from `survfit`. The numbers are placed at the bottom of the graph unless `y.n.risk` is given. If the fit is from `survest.psm`, `n.risk` does not apply.
`srt.n.risk` | angle of rotation for leftmost number of subjects at risk (since this number may run into the second or into the y-axis). Default is `0`.
`adj.n.risk` | justification for leftmost number at risk. Default is `1` for right justification. Use `0` for left justification, `.5` for centered.
`sep.n.risk` | multiple of upper y limit - lower y limit for separating lines of text containing number of subjects at risk. Default is `.056*(ylim[2]-ylim[1])`.
`y.n.risk` | When `n.risk=TRUE`, the default is to place numbers of patients at risk above the x-axis. You can specify a y-coordinate for the bottom line of the numbers using `y.n.risk`.
`cex.n.risk` | character size for number of subjects at risk (when `n.risk` is `TRUE`)
`dots` | set to `TRUE` to plot a grid of dots. Will be plotted at every `time.inc` (see `cph`) and at survival increments of .1 (if `d>.4`), .05 (if `.2 < d <= .4`), or .025 (if `d <= .2`), where `d` is the range of survival displayed.
`dotsize` | size of dots in inches
`grid` | defaults to `FALSE`. Set to a color shading to plot faint lines. Set to `1` to plot solid lines. Default is `.05` if `TRUE`.
`pr` | set to `TRUE` to print survival curve coordinates used in the plots

### Details

`survplot` will not work for Cox models with time-dependent covariables. Use `survest` or `survfit` for that purpose.

Use `ps.slide`, `win.slide`, `gs.slide` to set up nice defaults for plotting. These also set a system option `mgp.axis.labels` to allow x and y-axes to have differing `mgp` graphical parameters (see `par`). This is important when labels for y-axis tick marks are to be written horizontally (`par(las=1)`), as a larger gap between the labels and the tick marks are needed. You can set the axis-specific 2nd component of `mgp` using `mgp.axis.labels(c(xvalue,yvalue))`.

### Value

list with components adjust (text string specifying adjustment levels) and `curve.labels` (vector of text strings corresponding to levels of factor used to distinguish curves). For `survfit`, the returned value is the vector of strata labels, or NULL if there are no strata.

### Side Effects

plots. If `par()$mar[4]<4`, issues `par(mar=)` to increment `mar[4]` by 2 if `n.risk=TRUE` and `add=FALSE`. The user may want to reset `par(mar)` in this case to not leave such a wide right margin for plots. You usually would issue `par(mar=c(5,4,4,2)+.1)`.

### See Also

`datadist`, `Design`, `cph`, `psm`, `survest`, `predict.Design`, `plot.Design`, `units`, `errbar`, `survfit`, `survreg.distributions`, `labcurve`, `mgp.axis.labels`, `par`, `ps.slide`

### Examples

    # Simulate data from a population model in which the log hazard
    # function is linear in age and there is no age x sex interaction
    n <- 1000
    set.seed(731)
    age <- 50 + 12*rnorm(n)
    label(age) <- "Age"
    sex <- factor(sample(c('male','female'), n, TRUE))
    cens <- 15*runif(n)
    h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
    dt <- -log(runif(n))/h
    label(dt) <- 'Follow-up Time'
    e <- ifelse(dt <= cens,1,0)
    dt <- pmin(dt, cens)
    units(dt) <- "Year"
    dd <- datadist(age, sex)
    options(datadist='dd')
    S <- Surv(dt,e)

    #Plot stratified survival curves by sex, adj for quadratic age effect
    # with age x sex interaction (2 d.f. interaction)

    f <- cph(S ~ pol(age,2)*strat(sex), surv=TRUE)
    #or f <- psm(S ~ pol(age,2)*sex)

    survplot(f, sex=NA, n.risk=TRUE)           #Adjust age to median
    survplot(f, sex=NA, logt=TRUE, loglog=TRUE)   #Check for Weibull-ness (linearity)
    survplot(f, sex=c("male","female"), age=50)
                                            #Would have worked without datadist
                                            #or with an incomplete datadist
    survplot(f, sex=NA, label.curves=list(keys=c(2,0), point.inc=2))
                                            #Identify curves with symbols

    survplot(f, sex=NA, label.curves=list(keys=c('m','f')))
                                            #Identify curves with single letters

    #Plots by quintiles of age, adjusting sex to male
    options(digits=3)
    survplot(f, age=quantile(age,seq(0,1,by=.2)), sex="male")

    #Plot survival Kaplan-Meier survival estimates for males
    f <- survfit(S, subset=sex=="male")
    survplot(f)

    #Plot survival for both sexes
    f <- survfit(S ~ sex)
    survplot(f)
    #Check for log-normal and log-logistic fits
    survplot(f, fun=qnorm, ylab="Inverse Normal Transform")
    survplot(f, fun=function(y)log(y/(1-y)), ylab="Logit S(t)")

    options(datadist=NULL)

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