Survival analysis is time-to-event analysis, that is, when the outcome of interest is the time until an event occurs. Examples of time-to-events are the time until infection, reoccurrence of a disease, or recovery in health sciences; the duration of unemployment in economics; the time until the failure of a machine part or the lifetime of light bulbs in engineering, and so on.
To perform survival analysis:
Fit a model to your data. Use one or more of the functions listed on this page under Lifetime Data Analysis or Cox Proportional Hazards Models.
Plot or otherwise analyze the fitted model using the methods in the examples listed on this page under Topics, or using Cox Proportional Hazards Models functions.
fitcox function provides an object-oriented way to fit a Cox
proportional hazards model. The resulting
CoxModel object contains many statistics and methods for analysis.
coxphfit is an older function for
fitting Cox models that also enables code generation.
|Kernel smoothing function estimate for univariate and bivariate data|
|Maximum likelihood estimates|
|Asymptotic covariance of maximum likelihood estimators|
|Extreme value parameter estimates|
|Exponential parameter estimates|
|Gamma parameter estimates|
|Lognormal parameter estimates|
|Normal parameter estimates|
|Weibull parameter estimates|
|Fit probability distribution object to data|
|Open Distribution Fitter app|
|Cox proportional hazards regression|
|Create Cox proportional hazards model|
|Confidence interval for Cox proportional hazards model coefficients|
|Estimate Cox model hazard relative to baseline|
|Linear hypothesis tests on Cox model coefficients|
|Plot survival function of Cox proportional hazards model|
|Calculate survival of Cox proportional hazards model|
|Cox proportional hazards model|
Learn about censoring, survival data, and the survivor and hazard functions.
Find the empirical survivor functions and the parametric survivor functions using the Burr type XII distribution fit on data for two groups.
Estimate and plot the cumulative hazard and survivor functions for different groups.
Estimate the empirical hazard, survivor, and cumulative distribution functions.
Adjust survival rate estimates to quantify the effect of predictor variables.
Create data for a Cox model with three stratification levels, then fit and analyze the resulting model.
Create a Cox proportional hazards model, and assess the significance of the predictor variables.
Convert survival data to counting process form, and then construct a Cox proportional hazards model with time-dependent covariates.
Analyze lifetime data with censoring by modeling the time to failure of a throttle from an automobile fuel injection system.