Nonparametric and semiparametric methods for analyzing reliability and survival data

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.

The `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.

## Functions

expand all

 `ksdensity` Kernel smoothing function estimate for univariate and bivariate data `mle` Maximum likelihood estimates `mlecov` Asymptotic covariance of maximum likelihood estimators `evfit` Extreme value parameter estimates `expfit` Exponential parameter estimates `gamfit` Gamma parameter estimates `lognfit` Lognormal parameter estimates `normfit` Normal parameter estimates `wblfit` Weibull parameter estimates `fitdist` Fit probability distribution object to data `distributionFitter` Open Distribution Fitter app
 `ecdf` Empirical cumulative distribution function `ecdfhist` Histogram based on empirical cumulative distribution function `plotSurvival` Plot survival function of Cox proportional hazards model (Since R2021a) `probplot` Probability plots `wblplot` Weibull probability plot

#### Fit Cox Proportional Hazards Model

 `coxphfit` Cox proportional hazards regression

#### Fit `CoxModel` Object

 `fitcox` Create Cox proportional hazards model (Since R2021a)

#### `CoxModel` Methods

 `coefci` Confidence interval for Cox proportional hazards model coefficients (Since R2021a) `discardResiduals` Remove residuals from Cox model (Since R2022b) `hazardratio` Estimate Cox model hazard relative to baseline (Since R2021a) `linhyptest` Linear hypothesis tests on Cox model coefficients (Since R2021a) `plotSurvival` Plot survival function of Cox proportional hazards model (Since R2021a) `survival` Calculate survival of Cox proportional hazards model (Since R2021a)

## Objects

 `CoxModel` Cox proportional hazards model (Since R2021a)