predictRisk: Extrating predicting risks from regression models

In bozenne/riskRegressionLight: Light version of the package riskRegression

Description

Extract event probabilities from fitted regression models and machine learning objects.

Usage

## S3 method for class 'glm'
predictRisk(object,newdata,...)
## S3 method for class 'cph'
predictRisk(object,newdata,times,...)
## S3 method for class 'coxph'
predictRisk(object,newdata,times,...)
## S3 method for class 'matrix'
predictRisk(object,newdata,times,cause,...)
## S3 method for class 'survfit'
predictRisk(object,newdata,times,...)
## S3 method for class 'prodlim'
predictRisk(object,newdata,times,cause,...)
## S3 method for class 'CauseSpecificCox'
predictRisk(object,newdata,times,cause,...)

Arguments

object	A fitted model from which to extract predicted event probabilities
newdata	A data frame containing predictor variable combinations for which to compute predicted event probabilities.
...	Additional arguments that are passed on to the current method.
times	A vector of times in the range of the response variable, for which the cumulative incidences event probabilities are computed.
cause	Identifies the cause of interest among the competing events.

Details

The function predictRisk is a generic function, meaning that it invokes specifically designed functions depending on the 'class' of the first argument.

See predictRisk.

In uncensored binary outcome data there is no need to choose a time point.

When operating on models for survival analysis (without competing risks) the function still predicts the risk, as 1 - S(t|X) where S(t|X) is survival chance of a subject characterized by X.

When there are competing risks (and the data are right censored) one needs to specify both the time horizon for prediction (can be a vector) and the cause of the event. The function then extracts the absolute risks F_c(t|X) aka the cumulative incidence of an event of type/cause c until time t for a subject characterized by X. Depending on the model it may or not be possible to predict the risk of all causes in a competing risks setting. For example. a cause-specific Cox (CSC) object allows to predict both cases whereas a Fine-Gray regression model (FGR) is specific to one of the causes.

Value

For binary outcome a vector with predicted risks. For survival outcome with and without competing risks a matrix with as many rows as NROW(newdata) and as many columns as length(times). Each entry is a probability and in rows the values should be increasing.

Author(s)

Thomas A. Gerds [e-mail]

bozenne/riskRegressionLight documentation built on May 7, 2019, 12:52 a.m.