---
title: "Marginal modelling of clustered survival data"
author: Klaus Holst & Thomas Scheike
date: "`r Sys.Date()`"
output:
  rmarkdown::html_vignette:
    fig_caption: yes
    # fig_width: 7.15
    # fig_height: 5.5        
vignette: >
  %\VignetteIndexEntry{Marginal modelling of clustered survival data}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  ##dev="png",
  dpi=50,
  fig.width=7.15, fig.height=5.5,
  out.width="600px",
  fig.retina=1,
  comment = "#>"  
)
library(mets)
```

Overview 
========

A basic component of our modelling of multivariate survival data is that many models are
built around marginals on Cox form. The marginal Cox model can be fitted efficiently
in the mets package, in particular the handling of strata and robust standard errors is 
optimized. 

The basic models assumes that each subject has a marginal on Cox-form
$$ 
\lambda_{g(k,i)}(t) \exp( X_{ki}^T \beta).
$$ 
where $g(k,i)$ gives the strata for the subject. 

We here discuss and show how to get robust standard errors of  

* the regression parameters

* the baseline 

and how to do goodness of fit test using 

* cumulative residuals score test 

First we generate some data from the Clayton-Oakes model, with $5$ members
in each cluster and a variance parameter at $2$  


```{r}
 library(mets)
 options(warn=-1)
 set.seed(1000) # to control output in simulations for p-values below.
 n <- 1000
 k <- 5
 theta <- 2
 data <- sim_ClaytonOakes(n,k,theta,0.3,3)
```

The data has one subject per row.

* time : time of event 
* status : 1 for event and 0 for censoring 
* x : x is a binary covariate 
* cluster : cluster 


Now we fit the model and produce robust standard errors for both 
regression parameters and baseline.

First, recall that the baseline for strata $g$ is asymptotically equivalent to 
\begin{align}
\hat A_g(t) - A_g(t)  & = \sum_{k \in g} \left( \sum_{i: ki \in g} \int_0^t \frac{1}{S_{0,g}} dM_{ki}^g  - P^g(t) \beta_k \right) 
\end{align}
with $P^g(t) = \int_0^t E_g(s) d \hat \Lambda_g(s)$ the derivative of $\int_0^t 1/S_{0,g}(s) dN_{\cdot g}$
wrt to $\beta$, and 
\begin{align}
\hat \beta  - \beta  & = I(\tau)^{-1} \sum_k ( \sum_i \int_0^\tau (Z_{ki} - E_{g}) dM_{ki}^g ) =  \sum_k \beta_{k} 
\end{align}
with 
\begin{align}
 M_{ki}^g(t) &  = N_{ki}(t) - \int_0^t Y_{ki}(s) \exp( Z_{ki} \beta) d \Lambda_{g(k,i)}(t), \\
 \beta_{k} & =  I(\tau)^{-1} \sum_i \int_0^\tau (Z_{ki} - E_{g}) dM_{ki}^g 
\end{align}
the basic 0-mean processes, that are martingales in the iid setting,
and $I(t)$ is the derivative of the total score, $\hat U(t,\beta))$, 
with respect to $\beta$ evaluated at time $t$. 
       
The variance of the baseline of strata g  is estimated by 
\begin{align}
\sum_{k \in g} ( \sum_{i: ki \in g} \int_0^t \frac{1}{S_{0,g(k,i)}} d\hat M_{ki}^g - P^g(t) \beta_k )^2
\end{align}
that can be computed using the particular structure of 
\begin{align}
d \hat M_{ik}^g(t) & =  dN_{ik}(t) -  \frac{1}{S_{0,g(i,k)}} \exp(Z_{ik} \beta) dN_{g.}(t) 
\end{align}

This robust variance of the baseline and the iid decomposition for $\beta$ is
computed in mets as: 
```{r}
   out <- phreg(Surv(time,status)~x+cluster(cluster),data=data)
   summary(out)
   # robust standard errors attached to output
   rob <- robust_phreg(out)
```

We can get the iid decomposition of the $\hat \beta - \beta$ by 

```{r}
   # making iid decomposition of regression parameters
   betaiid <- IC(out)
   head(betaiid)
   # robust standard errors
   crossprod(betaiid/NROW(betaiid))^.5
   # same as 
```

We now look at the plot  with robust standard errors 


```{r}
  plot(rob,se=TRUE,robust=TRUE,col=3)
```

We can also make survival prediction with robust standard errors using the phreg.

```{r}
  pp <-  predict(out,data[1:20,],se=TRUE,robust=TRUE)
  plot(pp,se=TRUE,whichx=1:10)
```


Finally, just to check that we can recover the model we also estimate the dependence parameter 

```{r}
tt <- twostageMLE(out,data=data)
summary(tt)
```

Goodness of fit 
===============

The observed score process is given by 
\begin{align}
U(t,\hat \beta) & = \sum_k \sum_i \int_0^t (Z_{ki} - \hat E_g ) d \hat M_{ki}^g 
\end{align}
where $g$ is strata $g(k,i)$. The observed score has the iid decomposition 
\begin{align}
\hat U(t) = \sum_k \sum_i  \int_0^t (Z_{ki} - E_g) dM_{ki}^g  - 
	I(t)  \sum_k \beta_k 
\end{align}
where $\beta_k$ is the iid decomposition of the score process for the true $\beta$
\begin{align}
\beta_k  & =  I(\tau)^{-1} \sum_i \int_0^\tau (Z_{ki} - E_g ) d  M_{ki}^g 
\end{align}
and $I(t)$ is the derivative of the total score, $\hat U(t,\beta))$, 
with respect to $\beta$ evaluated at time $t$. 

This observed score can be resampled given it is on iid form in terms of 
clusters. 

Now using the cumulative score process for checking proportional 
hazards 
```{r}
gout <- gof(out)
gout
```

The p-value reflects whether the observed score process is consistent with
the model. 

```{r}
  plot(gout)
```

Computational aspects
--------------------

The score processes can be resampled as in Lin, Wei, Ying (1993) using the martingale structure, such that the observed
score process is resampled by 
\begin{align}
  \sum_k \sum_i \int_0^t g_{ki} (Z_{ki} - E_g) dN_{ki}  - I(t)  I^{-1}(\tau) g_{ki} \int_0^{\tau} (Z_{ki} - E_g) dN_{ki} .
\end{align}
where $g_{ki}$ are i.i.d. standard normals. 


Based on the zero mean processes we more generally with clusters can 
resample the score process.
For resampling of score process we need
\begin{align}
U(t,\beta) & = \sum_k \sum_i g_k \int_0^t (Z_{ki} - E_g ) dM_{ki}^g 
\end{align}
where $g$ is strata. We write $g_k$ as $g_{ki}$ and thus repeating $g_k$ within each cluster.


Computations are done using that 
\begin{align*}
\int_0^t (Z_{ki} - E_{g}) dM_{ki}^g & = \int_0^t (Z_{ki} - E_{g}) dN_{ki}^g - \int_0^{t} (Z_{ki} - E_{g})  Y_{ki}(u) d\Lambda^g(u)  
\end{align*}
therefore and summing the compensator part with the $g_{ki}$ multipliers then gives for each strata $g$
\begin{align*}
   &  \int_0^t \frac{S_{1g}^w(u)}{S_{0g}(u)} dN_{g.}(v) -  \int_0^t E_{g}(u) \frac{S_{0g}^w(u)}{S_{0g}(u)} dN_{g.}(v)
\end{align*}
with  
\begin{align*}
S_{jg}^w(t) & =  \sum_{ki \in g} \exp(Z_{ki} \beta) Z_{ki}^j Y_{ki}(t) g_{ki}  \\
S_{jg}(t) & =  \sum_{ki \in g} \exp(Z_{ki} \beta) Z_{ki}^j Y_{ki}(t).
\end{align*}



Cluster stratified Cox models
==============================

For clustered data it is possible to estimate the regression coefficient
within clusters by using Cox's partial likelihood stratified on clusters.

Note, here that the data is generated with a different subject specific structure, 
so we will not recover $\beta = 0.3$ and the model will not be
a proportional Cox model; we would therefore also expect to reject "proportionality" with
the goodness-of-fit test.

The model can be thought of as 
\[ 
\lambda_{g(k,i)} (t) \exp( X_{ki}^T \beta)
\] 
where $\lambda_g(t)$ is some cluster specific baseline. 

The regression coefficient $\beta$ can be estimated by using the 
partial likelihood for clusters. 

```{r}
 out <- phreg(Surv(time,status)~x+strata(cluster),data=data)
 summary(out)
```

SessionInfo
============


```{r}
sessionInfo()
```
