---
title: "Biostatistics III in R"
author:
- Code by Andreas Karlsson and Mark Clements
output:
  prettydoc::html_pretty:
    theme: cayman
    highlight: github
---

# Cox model for cause-specific mortality for melanoma (all stages) #


Use Cox regression to model the cause-specific survival of patients with skin melanoma (including all stages).

-----------


```{r setup, cache=FALSE, message=FALSE, echo=FALSE}
library('knitr')
read_chunk('../q12.R')
opts_chunk$set(cache=FALSE)
```

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.

```{r loadDependencies, message=FALSE}
```
Load the melanoma data and explore it.

```{r loadPreprocess, results='hide'}
```

## (a) ## 
Without adjusting for potential confounders, we see that
        females have a 37% lower mortality rate than males.

```{r 12.a, warning=FALSE, message=FALSE}
```

## (b) ## 
After adjusting for a range of potential confounders, we see that
        the estimated difference in cancer-specific mortality between
        males and females has decreased slightly but there is still
        quite a large difference.
		
```{r 12.b, warning=FALSE, message=FALSE}
```

## (c) ##

Let’s first estimate the effect of gender for each age group
        without adjusting for confounders.
		
```{r 12.c, warning=FALSE, message=FALSE}
```

We see that there is some evidence that the survival advantage
        experienced by females depends on age. The hazard ratio for
        males/females in the youngest age group is 0.46, while in the
        highest age group the hazard ratio is 0.68. There is evidence
        that the hazard ratios for gender differ across the age
        groups (p=0.0037). However, after adjusting for stage, subsite,
        and period there is no longer evidence of an interaction.
		
There is not strong evidence in support of the
        hypothesis (although some may consider that there is
        weak evidence).
		
## (d) ##
		
After having fitted a main effects model we can check the
        proportional hazards assumption by fitting a regression line
        through the model-based Schoenfeld residulas and check if the
        slope is statistically different from zero.

There is strong evidence that the proportional hazard assumption
		is not satisfied for the effect of stage. Unless our primary
        interest is in the stage effect we can fit a stratified Cox
        model where we stratify on stage (i.e. estimate a separate
        baseline hazard function for each stage group).		

```{r 12.d.i, warning=FALSE, message=FALSE}
```

If we re-do a test for non-proportional hazards we find that
        there is no longer evidence that any of the remaining covariates
        effects seem to depend on time since diagnosis.

Having accounted for the time-dependent effect of stage, there
        is still no evidence that the effect of sex is modified by age
        at diagnosis.

```{r 12.d.ii, warning=FALSE, message=FALSE}
```

If you have time make sure you check for additional interaction
        terms between the remaining covariates, i.e. between age at
        diagnosis and stage.

```{r 12.d.iii, warning=FALSE, message=FALSE}
```
