DynNom (version 4.0)

DynNom.coxph: Dynamic Nomograms for Proportional Hazards Models

Description

DynNom.coxph displays the results of a coxph model object as a dynamic nomogram in an 'RStudio' panel or web browser.

Usage

DynNom.coxph(model, data, clevel = 0.95, covariate = c("slider", "numeric"), ptype = c("st", "1-st"))

Arguments

model

a coxph model object

data

dataframe containing the accompanying data

clevel

confidence level required

covariate

The option to choose the type of covariate(s) input control widget for numeric values. If "slider" (the default) is chosen a shiny application with slider control widgets are used while if "numeric" is chosen numeric values input controls will be displayed.

ptype

If "st" (the default) is chosen, a plot of the estimated survivor function, S(t), is displayed. If "1-st" is chosen a plot of 1- S(t) is displayed.

Value

A dynamic nomogram in a shiny application which recognises all the predictors in the model and uses them to build a sidebar panel. It sets up drop down menus for factors and sliders set at the mean and bounded by the range for covariates.The individual predictions with a relative confidence interval are calculated using the predict function, displaying graphically as either the Kaplan-Meier in the Estimated S(t) tab or the Predicted Survival tab. Table of individual predictions and model output are available in the Numerical Summary and Model Summary tabs, respectively.

See Also

coxph, predict.coxph

Examples

## Not run: 
# data.kidney <- kidney
# # always make sure that the categorical variables are in a factor class
# data.kidney$sex <- as.factor(data.kidney$sex)
# levels(data.kidney$sex) <- c("male", "female")
# 
# model1 <- coxph(Surv(time, status) ~ age + sex + disease, data.kidney)
# DynNom(model1, data.kidney)
# DynNom(model1, data.kidney, ptype = "1-st")
# 
# # a cox model including a strata term
# data(lung)
# model2 <- coxph(Surv(time, status) ~ age + strata(sex) + ph.ecog , data = lung)
# DynNom(model2, lung)
# ## End(Not run)

if (interactive()) {
data.ovary <- ovarian
data.ovary$resid.ds <- as.factor(data.ovary$resid.ds)
levels(data.ovary$resid.ds) <- c("no", "yes")
data.ovary$rx <- as.factor(data.ovary$rx)
data.ovary$ecog.ps <- as.factor(data.ovary$ecog.ps)

model3 <- coxph(Surv(futime, fustat) ~ age + resid.ds * rx + ecog.ps, data = data.ovary)
DynNom(model3, data.ovary)
}