R: Additive Cox Proportional Hazard Model

cox.ph {mgcv} | R Documentation

## Additive Cox Proportional Hazard Model

### Description

The `cox.ph` family implements the Cox Proportional Hazards model with Peto's correction for ties, and estimation by penalized partial likelihood maximization, for use with `gam`. In the model formula, event time is the response. The `weights` vector provides the censoring information (0 for censoring, 1 for event).

### Usage

    cox.ph(link="identity")

### Arguments

`link` | currently (and possibly for ever) only `"identity"` supported.

### Details

Used with `gam` to fit Cox Proportional Hazards models to survival data. The model formula will have event/censoring times on the left hand side and the linear predictor specification on the right hand side. Censoring information is provided by the `weights` argument to `gam`, with 1 indicating an event and 0 indicating censoring.

Prediction from the fitted model object (using the `predict` method) with `type="response"` will predict on the survivor function scale. See example code below for extracting the baseline hazard/survival directly. Martingale or deviance residuals can be extracted. The `fitted.values` stored in the model object are survival function estimates for each subject at their event/censoring time.

Estimation of model coefficients is by maximising the log-partial likelihood penalized by the smoothing penalties. See e.g. Hastie and Tibshirani, 1990, section 8.3. for the partial likelihood used (with Peto's approximation for ties), but note that optimization of the partial likelihood does not follow Hastie and Tibshirani. See Klein amd Moeschberger (2003) for estimation of residuals, the baseline hazard, survival function and associated standard errors.

The percentage deviance explained reported for Cox PH models is based on the sum of squares of the deviance residuals, as the model deviance, and the sum of squares of the deviance residuals when the covariate effects are set to zero, as the null deviance. The same baseline hazard estimate is used for both.

### Value

An object inheriting from class `general.family`.

### References

Hastie and Tibshirani (1990) Generalized Additive Models, Chapman and Hall.

Klein, J.P and Moeschberger, M.L. (2003) Survival Analysis: Techniques for Censored and Truncated Data (2nd ed.) Springer.

### Examples

    library(mgcv)
    library(survival) ## for data
    col1 <- colon[colon$etype==1,] ## concentrate on single event
    col1$differ <- as.factor(col1$differ)
    col1$sex <- as.factor(col1$sex)

    b <- gam(time~s(age,by=sex)+sex+s(nodes)+perfor+rx+obstruct+adhere,
             family=cox.ph(),data=col1,weights=status)

    summary(b)

    plot(b,pages=1,all.terms=TRUE) ## plot effects

    plot(b$linear.predictors,residuals(b))

    ## plot survival function for patient j...

    np <- 300;j <- 6
    newd <- data.frame(time=seq(0,3000,length=np))
    dname <- names(col1)
    for (n in dname) newd[[n]] <- rep(col1[[n]][j],np)
    newd$time <- seq(0,3000,length=np)
    fv <- predict(b,newdata=newd,type="response",se=TRUE)
    plot(newd$time,fv$fit,type="l",ylim=c(0,1),xlab="time",ylab="survival")
    lines(newd$time,fv$fit+2*fv$se.fit,col=2)
    lines(newd$time,fv$fit-2*fv$se.fit,col=2)

    ## crude plot of baseline survival...

    plot(b$family$data$tr,exp(-b$family$data$h),type="l",ylim=c(0,1),
         xlab="time",ylab="survival")
    lines(b$family$data$tr,exp(-b$family$data$h + 2*b$family$data$q^.5),col=2)
    lines(b$family$data$tr,exp(-b$family$data$h - 2*b$family$data$q^.5),col=2)
    lines(b$family$data$tr,exp(-b$family$data$km),lty=2) ## Kaplan Meier

    ## Simple simulated known truth example...
    ph.weibull.sim <- function(eta,gamma=1,h0=.01,t1=100) {
      lambda <- h0*exp(eta)
      n <- length(eta)
      U <- runif(n)
      t <- (-log(U)/lambda)^(1/gamma)
      d <- as.numeric(t <= t1)
      t[!d] <- t1
      list(t=t,d=d)
    }
    n <- 500;set.seed(2)
    x0 <- runif(n, 0, 1);x1 <- runif(n, 0, 1)
    x2 <- runif(n, 0, 1);x3 <- runif(n, 0, 1)
    f0 <- function(x) 2 * sin(pi * x)
    f1 <- function(x) exp(2 * x)
    f2 <- function(x) 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
    f3 <- function(x) 0*x
    f <- f0(x0) + f1(x1) + f2(x2)
    g <- (f-mean(f))/5
    surv <- ph.weibull.sim(g)
    surv$x0 <- x0;surv$x1 <- x1;surv$x2 <- x2;surv$x3 <- x3

    b <- gam(t~s(x0)+s(x1)+s(x2,k=15)+s(x3),family=cox.ph,weights=d,data=surv)

    plot(b,pages=1)

[Package mgcv version 1.8-3 Index]