# Predictions for a relative survival model Predict linear predictors, hazard and cumulative hazard for a model fitted by `flexrsuv` ```r ## S3 method for class 'flexrsurv' predict(object, newdata= NULL, type = c("lp", "link", "risk", "hazard", "hazardrate", "rate", "loghazard", "log", "lograte", "cumulative.rate", "cumulative.hazard", "cumulative", "cum", "survival", "surv", "netsurv"), se.fit=FALSE, ci.fit = FALSE, level = .95, na.action=na.pass, ...) ``` ## Arguments - `object`: the results of a flexrsurv fit. - `newdata`: Optional new data at which to do predictions. If absent predictions are for the data frame used in the original fit. - `type`: the type of predicted value. Choices are the linear predictor (`"lp"`, `"log"`, `"loghazard"`, `"lograte"`), the hazard (`"rate"`, `"hazard"`, `"hazardrate"`, `"risk"`) or the cumulative hazard (`"cum"`, `"cumulative.hazard"`, `"cumulative"`). - `se.fit`: if TRUE, pointwise standard errors are produced for the predictions (not available for cumulative hazard). - `ci.fit`: if TRUE, confidence intervale are produced for the predictions. - `level`: Confidence level. - `na.action`: function determining what should be done with missing values in `newdata`. The default is to predict `NA`. - ``...``: For future methods ## Returns a vector or a list containing the predictions (element `"fit"`) and their standard errors (element `"se.fit"`) if the se.fit option is TRUE. ## Details For cumulative hazard, the cumulative hazard is computed from 0 until the given end time. The cumulative hazard is computed using the same numerical integration method as the one used to fit the model. ## Note To work correctly, arguments `Boundary.knots` and `Boundary.knots.t` must be included in the call to `NPH()`, `NLL()` and `NPHNLL()` in the formula of `flexrsurv` ## See Also `predict`, `flexrsurv`, `flexrsurvclt` ## Examples ```r if (requireNamespace("relsurv", quietly = TRUE)) { # data from package relsurv data(rdata, package="relsurv") # rate table from package relsurv data(slopop, package="relsurv") # get the death rate at event (or end of followup) from slopop for rdata rdata$iage <- findInterval(rdata$age*365.24+rdata$time, attr(slopop, "cutpoints")[[1]]) rdata$iyear <- findInterval(rdata$year+rdata$time, attr(slopop, "cutpoints")[[2]]) therate <- rep(-1, dim(rdata)[1]) for( i in 1:dim(rdata)[1]){ therate[i] <- slopop[rdata$iage[i], rdata$iyear[i], rdata$sex[i]] } rdata$slorate <- therate # change sex coding rdata$sex01 <- rdata$sex -1 # centering age rdata$agec <- rdata$age- 60 # fit a relative survival model with a non linear effect of age fit <- flexrsurv(Surv(time,cens)~sex01+NLL(age, Knots=60, Degree=3, Boundary.knots = c(24, 95)), rate=slorate, data=rdata, knots.Bh=1850, # one interior knot at 5 years degree.Bh=3, Spline = "b-spline", initbyglm=TRUE, int_meth= "BOOLE", step=50 ) summary(fit, correlation=TRUE) newrdata <- rdata newrdata$age <- rep(60, length(rdata$age)) newrdata$sex <- factor(newrdata$sex, labels=c("m", "f")) linpred <- predict(fit, newdata=newrdata, type="lp", ci.fit=TRUE) predhazard <- predict(fit, newdata=newrdata, type="hazard" , ci.fit=TRUE) predcumhazard <- predict(fit, newdata=newrdata, type="cum", ci.fit=TRUE) require(ggplot2) tmp <- cbind(newrdata, linpred) glp <- ggplot(tmp, aes(time, colour=sex)) glp + geom_ribbon(aes(ymin = lwr, ymax = upr, fill=sex)) + geom_line(aes(y=fit)) + scale_fill_manual(values = alpha(c("blue", "red"), .3)) tmp <- cbind(newrdata, predhazard) glp <- ggplot(tmp, aes(time, colour=sex)) glp + geom_ribbon(aes(ymin = lwr, ymax = upr, fill=sex)) + geom_line(aes(y=fit)) + scale_fill_manual(values = alpha(c("blue", "red"), .3)) tmp <- cbind(newrdata, predcumhazard) glp <- ggplot(tmp, aes(time, colour=sex)) glp + geom_ribbon(aes(ymin = lwr, ymax = upr, fill=sex)) + geom_line(aes(y=fit)) + scale_fill_manual(values = alpha(c("blue", "red"), .3)) } ```

predict.flexrsurv function