plotSurvival

Plot survival function of Cox proportional hazards model

Since R2021a

Syntax

plotSurvival(coxMdl)

plotSurvival(coxMdl,X)

plotSurvival(coxMdl,X,Stratification)

plotSurvival(coxMdl,ax,___)

plotSurvival(___,Name,Value)

graphics = plotSurvival(___)

Description

plotSurvival(coxMdl) plots the baseline survival function of a Cox proportional hazards model coxMdl. The survival function at time t is the estimated probability of survival until time t. The term baseline refers to the survival function at the determined baseline of the predictors. This value is stored in coxMdl.Baseline, and the default value is the mean of the data set used for training.

plotSurvival(coxMdl,X) plots the survival function when the predictors have the values in X. The plot includes one line for each row of X.

plotSurvival(coxMdl,X,Stratification) plots the survival function for the given value of the stratification variable Stratification. You must have one row in Stratification for each row in X.

Note

When you train coxMdl using stratification variables and pass predictor variables X, plotSurvival also requires you to pass stratification variables.

plotSurvival(coxMdl,ax,___) plots in the specified graphics axes ax using any of the input argument combinations in the previous syntaxes.

plotSurvival(___,Name,Value) specifies additional options using one or more name-value arguments. For example, plotSurvival(CoxMdl,"Time",T) plots the survival function at times T.

graphics = plotSurvival(___) returns an array of Stair graphics objects. See Stair Properties.

Examples

Plot Survival

Perform a Cox proportional hazards regression on the lightbulb data set, which contains simulated lifetimes of light bulbs. The first column of the light bulb data contains the lifetime (in hours) of two different types of bulbs. The second column contains a binary variable indicating whether the bulb is fluorescent or incandescent; 0 indicates the bulb is fluorescent, and 1 indicates it is incandescent. The third column contains the censoring information, where 0 indicates the bulb was observed until failure, and 1 indicates the observation was censored.

Fit a Cox proportional hazards model for the lifetime of the light bulbs, accounting for censoring. The predictor variable is the type of bulb.

load lightbulb
coxMdl = fitcox(lightbulb(:,2),lightbulb(:,1), ...
    'Censoring',lightbulb(:,3));

Plot the baseline survival function as a function of time t, meaning the probability that a light bulb fails after time t. By default, the baseline is calculated for the mean of the predictor, which in this case is mean(lightbulb(:,2)) = 0.5.

plotSurvival(coxMdl)

Plot the survival for fluorescent bulbs (predictor = 0) and incandescent bulbs (predictor = 1).

plotSurvival(coxMdl,[0;1])

To calculate the survival without plotting, use survival.

Survival Plot with Stratification Variables

Load the coxModel data. (This simulated data is generated in the example Cox Proportional Hazards Model Object.) The model named coxMdl has three stratification levels (1, 2, and 3) and a predictor X with three categorical values (1, 1/20, and 1/100).

load coxModel

Plot the survival for X = 1 at the three stratification levels.

c1 = categorical(1);
X = [c1;c1;c1];
stratification = [1;2;3];
plotSurvival(coxMdl,X,stratification)
xlim([1,30])

Log-Scaled Survival Plot

Load the coxModel data. (This simulated data is generated in the example Cox Proportional Hazards Model Object.) The model named coxMdl has three stratification levels (1, 2, and 3) and a predictor X with three categorical values (1, 1/20, and 1/100).

load coxModel

To enable programmatic editing of a survival plot, create an axes.

h = figure;
axes1 = axes('Parent',h);

Plot the survival function for the X predictor value categorical(1) and stratification level 3. This stratification level represents a constant hazard rate. When log-scaled, the resulting survival plot should, therefore, be close to a straight line. Plot for times 1 through 30.

oo = categorical(1);
plotSurvival(coxMdl,axes1,oo,3,'Time',linspace(1,30,300));
axes1.YScale = 'log';

Input Arguments

coxMdl — Fitted Cox proportional hazards model

CoxModel object

Fitted Cox proportional hazards model, specified as a CoxModel object. Create coxMdl using fitcox.

X — Predictors for model

mean of predictors used for training, but 0 for all categorical predictors (default) | array of predictors of type used for training

Predictors for the model, specified as an array of predictors of the same type used for training coxMdl. Each row of X represents one set of predictors.

Data Types: double | table | categorical

Stratification — Stratification level

variable or variables of type used for training

Stratification level, specified as a variable or variables of the same type used for training coxMdl. Specify the same number of rows in Stratification as in X.

Data Types: single | double | logical | char | string | table | cell | categorical

ax — Axes for plotting

graphics axes object

Axes for plotting, specified as a graphics axes object.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: plotSurvival(CoxMdl,Time=T)

ExtrapolationMethod — Extrapolation method for survival in out-of-range times

'nearest' (default) | 'linear' | 'next' | 'none' | 'previous'

Extrapolation method to compute the survival for out-of-range times, specified as one of the listed values. A CoxModel object uses the cumulative baseline hazard, stored in CoxModel.Hazard, to compute the baseline survival function in the survival or plotSurvival functions. For times within the range (defined next), results are from linear interpolation of the baseline survival function.

For a nonstratified model, the range is [T1,T2], where T1 is (1 - eps) times the earliest training time, and T2 is the latest training time. The ExtrapolationMethod for a time T gives the following result:

• 'nearest' (default) — If T < T1, the result is for time T1. If T > T2, the result is for time T2.

• 'linear' — The result is a linear extrapolation from the nearest time in the range. Extrapolated survival values are truncated to lie in [0,1]. In other words, if val is the returned survival value and extrapval is the linear extrapolation, then

  val = max(0, min(1,extrapval)).

• 'next' — If T < T1, the result is for time T1. If T > T2, the result is NaN.

• 'none' — If T < T1 or T > T2, the result is NaN.

• 'previous' — If T < T1, the result is NaN. If T > T2, the result is for time T2.

For each stratum in a stratified model, define the time range exactly as for a nonstratified model, using the event times in that stratum. The extrapolated values of survival in each stratum use the ExtrapolationMethod applied to the stratum range.

Example: 'next'

Data Types: char | string

Time — Times for survival estimates

coxMdl.Hazard(:,1) (default) | real vector

Times for survival estimates, specified as a real vector. plotSurvival sorts the specified times and converts them to a column vector, if necessary. For an unstratified model and times in the range of coxMdl.Hazard(:,1), the resulting values are linearly interpolated from times in the training data. For Time values outside the fitting data range, the survival is extrapolated using the extrapolation method specified in ExtrapolationMethod.

For stratified models, distinct time ranges for each stratum in coxMdl.Hazard(:,1) are separated by 0s in coxMdl.Hazard(:,2). plotSurvival estimates the survival in each stratum using the same procedure as for an unstratified model.

Example: 0:40

Data Types: double

Version History

Introduced in R2021a