# Biostatistics III in R

#### Code by Johan Zetterqvist and Mark Clements

# Exercise 13. Modelling the diet data using Cox regression

Now fit a model to the localised melanoma data where the outcome is observed survival (i.e. all deaths are considered to be events).

You may have to install the required packages the first time you use them. You can install a package by `install.packages("package_of_interest")` for each package you require.

    library(biostat3) # for Surv and survfit

Load the melanoma data and explore it.

## (a)

Fit the following Poisson regression model to the diet data (we fitted this same model in question 6). Now fit the following Cox model.

1. On what scale are we measuring ‘time’ ? That is, what is the timescale?
2. Is it correct to say that both of these models estimate the effect of high energy on CHD without controlling for any potential confounders? If not, how are these models conceptually different?
3. Would you expect the parameter estimates for these two models to be very different? Is there a large difference?

    ## y is the observed time
    ## so it already measures time since entry
    poisson13a <- glm( chd ~ hieng + offset( log( y ) ), family=poisson, data=diet)
    summary(poisson13a)
    eform(poisson13a)

    cox13a <- coxph(Surv(y, chd) ~ hieng, data=diet)
    summary(cox13a)

## (b)

Use the attained age as the timescale and refit the Cox model. Is the estimate of the effect of high energy different? Would we expect it to be different?

    ## counting process data with coxph
    scale <- 365.24
    cox13b <- coxph(Surv((doe-dob)/scale, (dox-dob)/scale, chd) ~ hieng, data=diet)
    summary(cox13b)