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


# Exercise 10. Examining the proportional hazards hypothesis (localised melanoma) #

-----------

Load the diet data using time-on-study as the timescale with a maximum of 10 years follow-up.

```{r setup, cache=FALSE, message=FALSE, echo=FALSE}
library('knitr')
read_chunk('../q10.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 melanoma data and explore it.

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

## (a) ## 

If we look at the hazard curves, at their peak the ratio is
approximately $0.038/0.048\approx0.79$. The ratio is similar at
other follow-up times.

```{r 10.a, message=FALSE}
```

## (b) ## 
There is no strong evidence against an assumption of
proportional hazards since we see (close to) parallel curves
when plotting the instantaneous cause-specific hazard on the
log scale.

```{r 10.b, message=FALSE}
```

## (c) ## 

If the proportional hazards assumption is appropriate then we
should see parallel lines. This looks
okay; we shouldn’t put too much weight on the fact that the
curves cross early in the follow-up since there are so few
deaths there. The difference between the two log-cumulative
hazard curves is similar during the part of the follow-up where
we have the most information (most deaths). Note that these
curves are not based on the estimated Cox model (i.e., they
are unadjusted).
		

```{r 10.c, message=FALSE}
```

## (d) ## 
The estimated hazard ratio from the Cox model is $0.78$ which is
similar (as it should be) to the estimate made by looking at the
hazard function plot.

```{r 10.d}
```

## (e) ## 
The plot of the scaled
Schoenfeld residuals for the effect of period. Under
proportional hazards, the smoother will be a horizontal line.
The line is not, however, perfectly horizontal; it appears that
the effect of period is greater earlier in the follow-up.

```{r 10.e}
```


## (f) ## 
No solution written for this part.

## (g) ## 
It seems that there is evidence of non-proportional hazards by
age (particularly for the comparison of the oldest to youngest)
but not for calendar period. The plot of Schoenfeld residuals
suggested non-proportionality for period but this was not
statistically significant.

```{r 10.g}
```

## (h) ## 

The hazard ratios for age in the top panel are for the first two
years subsequent to diagnosis. To obtain the hazard ratios for
the period two years or more following diagnosis we multiply the
hazard ratios in the top and bottom panel. That is, during the
first two years following diagnosis patients aged 75 years or
more at diagnosis have 5.4 times higher cancer-specific
mortality than patients aged 0–44 at diagnosis. During the
period two years or more following diagnosis the corresponding
hazard ratio is $5.4 \times 0.49=2.66$.\
Using `survSplit` to split on time will give you the same results
as above. We see that the age:follow up interaction is
statistically significant.


```{r 10.h}
```


The regression equation for the cox2p8Split1 model is 
\begin{align*}
h(t|\text{year8594},\text{sex},\text{agegrp},\text{fu}) &= h_0(t) \exp(\beta_1 I(\text{sex}=\text{"Female"})+\beta_2 I(\text{year8594}=\text{"Diagnosed 85-94"})+\\
&\qquad\beta_3 I(\text{agegrp}=\text{"45-59"})+\beta_4 I(\text{agegrp}=\text{"60-74"})+\beta_5 I(\text{agegrp}=\text{"75+"}) + \\
&\qquad \beta_6 I(\text{agegrp}=\text{"45-59"} \&\ \text{fu}=2)+\beta_7 I(\text{agegrp}=\text{"60-74"}  \&\ \text{fu}=2)+\beta_8 I(\text{agegrp}=\text{"75+"} \&\ \text{fu}=2))
\end{align*}
where $h(t|\text{year8594},\text{sex},\text{agegrp},\text{fu})$ is the hazard at time $t$ given covariates $\text{year8594}$, $\text{sex}$, $\text{agegrp}$ and $\text{fu}$, with baseline hazard $h_0(t)$ and regression coefficients representing log hazard ratios for $\beta_1$ for females, $\beta_2$ for the calendar period 1985--1994, $\beta_3$ for those aged 45--59 years at diagnosis, $\beta_4$ for those aged 60--74 years and $\beta_5$ for those aged 75 years and over, with interaction terms for the change in log hazard ratio for the second follow-up period being $\beta_6$ for those aged 45--59 years at diagnosis, $\beta_7$ for those aged 60--74 years and $\beta_8$ for those aged 75 years and over.


## (i) ## 

              0–2 years   2+ years
------------- ----------- ----------
  Agegrp0-44     1.00        1.00
  Agegrp45-59    1.70        1.23
  Agegrp60-74    2.46        1.70 
  Agegrp75+      5.42        2.67


```{r 10.i}
```

The regression equation for the `cox2p8Split2` model is 
\begin{align*}
h(t|\text{year8594},\text{sex},\text{agegrp},\text{fu}) &= h_0(t) \exp(\beta_1 I(\text{sex}=\text{"Female"})+\beta_2 I(\text{year8594}=\text{"Diagnosed 85-94"})+\\
&\qquad\beta_3 I(\text{agegrp}=\text{"45-59"} \&\ \text{fu}=1)+\beta_4 I(\text{agegrp}=\text{"45-59"} \&\ \text{fu}=2)+\beta_5 I(\text{agegrp}=\text{"60-74"} \&\ \text{fu}=1) + \\
&\qquad \beta_6 I(\text{agegrp}=\text{"60-74"} \&\ \text{fu}=2)+\beta_7 I(\text{agegrp}=\text{"75+"}  \&\ \text{fu}=1)+\beta_8 I(\text{agegrp}=\text{"75+"} \&\ \text{fu}=2))
\end{align*}
where $h(t|\text{year8594},\text{sex},\text{agegrp},\text{agegrp},\text{fu})$ is the hazard at time $t$ given covariates $\text{year8594}$, $\text{sex}$ and $\text{agegrp}$, $\text{agegrp}$ and $\text{fu}$, with baseline hazard $h_0(t)$ and regression coefficients representing log hazard ratios for $\beta_1$ for the calendar period 1985--1994, $\beta_2$ for females, with log hazard ratios for the first and second follow-up period being $\beta_3$ and $\beta_4$ for those aged 45--59 years at diagnosis, $\beta_5$ and $\beta_6$ for those aged 60--74 years and $\beta_7$ and $\beta_8$ for those aged 75 years and over.

We can also use the `tt` argument in `coxph` for modelling for time-varying effects:

```{r 10.ib}
```

The regression equation for the `cox2p8tvct` model is 
\begin{align*}
h(t|\text{year8594},\text{sex},\text{agegrp}) &= h_0(t) \exp(\beta_1 I(\text{sex}=\text{"Female"})+\beta_2 I(\text{year8594}=\text{"Diagnosed 85-94"})+\\
&\qquad\beta_3 I(\text{agegrp}=\text{"45-59"})+\beta_4 I(\text{agegrp}=\text{"60-64"}) + \beta_5 I(\text{agegrp}=\text{"75+"}) + \\
&\qquad \beta_6 I(\text{agegrp}=\text{"45-59"}) t +\beta_7 I(\text{agegrp}=\text{"60-74"})t + \beta_8 I(\text{agegrp}=\text{"75+"}) t)
\end{align*}
where $h(t|\text{year8594},\text{sex},\text{agegrp},\text{agegrp})$ is the hazard at time $t$ given covariates $\text{year8594}$, $\text{sex}$ and $\text{agegrp}$ and $\text{agegrp}$, with baseline hazard $h_0(t)$ and regression coefficients representing log hazard ratios for $\beta_1$ for the calendar period 1985--1994, $\beta_2$ for females, with log hazard ratios at time 0 for those aged 45--59 years, 60--74 years and 75 years and over are $\beta_3$, $\beta_4$ and $\beta_5$, respectively, while the change in log hazard ratios per year for those aged those aged 45--59 years, 60--74 years and 75 years and over are $\beta_3$, $\beta_4$ and $\beta_5$, respectively.

The hazard ratio for model `cox2p8tvct` for the those aged 75 years and over compared with those aged less than 45 years is 
\begin{align*}
\frac{h(t|\text{year8594},\text{sex},\text{agegrp}=\text{"75+"})}{h(t|\text{year8594},\text{sex},\text{agegrp}=\text{"0-44"})} &= 
\frac{h_0(t) \exp(\beta_1 I(\text{sex}=\text{"Female"})+\beta_2 I(\text{year8594}=\text{"Diagnosed 85-94"})+ \beta_5 + \beta_8 t)}{h_0(t)\exp(\beta_1 I(\text{sex}=\text{"Female"})+\beta_2 I(\text{year8594}=\text{"Diagnosed 85-94"}))} \\
&= \exp(\beta_5 + \beta_8 t)
\end{align*}

We have shown several ways to use the `tt` functionality for a factor variable, including using different tt arguments for each factor level (as per model `cox2p8tvc2`) and using a `tt` term that returns a set of columns (as per model `cox2p8tvct`). We have used the `lincom` function to estimate the hazard ratio for `agegrp75+`. We will later describe a more flexible approach to modelling time-dependent effects using `stpm2`.

## (j) ## 

```{r 10.j}
```

