\documentclass{article}
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\usepackage{Sweave}
\usepackage{amsmath}
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\SweaveOpts{keep.source=TRUE, fig=FALSE}
%\VignetteIndexEntry{Mixed Effects Cox Models}
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\newcommand{\myfig}[1]{\resizebox{\textwidth}{!}
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\title{Mixed Effects Cox Models}
\author{Terry Therneau \\Mayo Clinic}

\begin{document}
\maketitle

\section{Introduction}

Like many other projects, this software library started
with a data set and a problem. 
From this came statistical ideas for a solution, followed by some initial
programming --- which more than anything else helped to define the
\emph{real} computational and statistical issues --- and then a more
ambitious programming solution.
The problem turned out to be harder than
I thought; the first release-worthy code took over 3 years in gestation
and resulted in the \emph{kinship} library.
This in turn led to application to a larger set of problems, and
a complete re-design of the underlying code.  It was then
split into 
separate libraries: coxme contains the central code for mixed effects
Cox and linear models, kinship2 contains code for the construction and
manipulation of pedigree data, and bdsmatrix contains underlying
routines for block-diagonal sparse matrices.  

\section{Model}
The coxme function fits the model
\begin{align*}
  \lambda(t) &= \lambda_0(t) e^{X\beta + Zb} \\
      b &\sim G(0, \Sigma(\theta))
\end{align*}
where $\lambda_0$ is an unspecified baseline hazard function,
$X$ and $Z$ are the design matrices for the
fixed and random effects, respectively,
$\beta$ is the vector of fixed-effects coefficients and 
$b$ is the vector of random effects coefficients.
The random effects distribution $G$ is modeled as Gaussian with 
mean zero and a variance matrix $\Sigma$,
which in turn depends
a vector of parameters $\theta$.  

For any fixed values of $\beta$ and $b$ we define the usual
Cox partial likelihood PL (often labeled as a log-likelihood in
printouts) as
\begin{equation*}
\log[PL(\beta,b))] = \sum_{i=1}^n \int_0^\infty \left[
     Y_i(t) \eta_i(t) - \log \left(\sum_j Y_j(t)e^{\eta_j(t)} \right)\right]
\end{equation*}
where $\eta_i(t) = X_i(t)\beta + Z_i(t)b$ is the linear score for
subject $i$ at time $t$ and $Y_i(t)$ describes the risk set,
$Y_i(t) = 1$ if subject $i$ is still under
observation at time $t$ and 0 otherwise.
For further details see chapter 3 of Therneau and Grambsch \cite{Therneau00}
or any other mid-level survival text.     

We can integrate out the random effects to create the integrated partial
likelihood
\begin{equation*}
IPL(\beta, \theta) = \frac{1}{(2\pi)^{q/2}|\Sigma(\theta)|^{1/2}} 
         \int PPL(\beta, b) e^{-b'\Sigma^{-1}(\theta)b/2}\,db
\end{equation*} 
where $q$ is the length of $b$, i.e., the number of random effects.
Ripatti and Palmgren \cite{Ripatti02} showed that the IPL can be
treated as a likelihood, just as integrated full likelihoods
can.  An ML estimate is obtained by joint maximization over
$\beta$ and $\theta$.
We can compare a nested set of random or fixed effects Cox
models using chi-square tests.

The REML estimate is obtained by also integrating out the fixed effects
\begin{equation*}
REML(\theta) = \frac{1}{(2\pi)^{q/2}|\Sigma(\theta)|^{1/2}} 
         \int PPL(\beta, b) e^{-b'\Sigma^{-1}(\theta)b/2}\,db \,d\beta
\end{equation*}
The REML estimate is obtained by maximization of this quantity over
$\theta$.
As in linear models, the REML estimate cannot be used for testing fixed
effects, due to the lack of a proper prior distribution for 
$\beta$.  
For instance, assume that \texttt{age} were a candidate variable.  The
REML value for a fit with age in days versus one with age in years
will differ by the constant $\log(365.25)$;
this has no impact on the estimate of $\theta$, but makes the comparison
of two models, one with and one without age, completely arbitrary.

In linear models the REML estimate is often preferred, since practical
experience has shown it to be more reliable; the ML estimates are often
  too large.  
By analogy, some have recommended using a REML estimate for the mixed
effects Cox model.  However, either theoretical and practical evidence for
its superiority is sparse.  
The coxme function currently only provides an ML estimate. 
 
For a very simple fit such as
<<eval=FALSE, echo=TRUE>>=
fit1 <- coxme(Surv(endage, cancer) ~ parity + (1| famid))
@ 
found below there will be one random intercept per
group (family), and 
$Z$ will be the usual design matrix for a
one-way anova, i.e., the same design matrix as would
be used by a linear model \texttt{lm(y $\sim$ factor(famid)-1)}.
In this case $Z_{ij} =1$ iff subject $i$ is a member of family $j$.
The variance of the random effects in this case is
$\Sigma= \theta I$.

Note that if there are $k$ groups the vector of random effects
coefficients $b$ will have $k$ elements.
Because of the shrinkage provided by the random effect
they will satisfy $\sum b_k =0$, rather than having one of
their members set to zero.
With respect to the random effects the \texttt{contrasts}
option has no relevance.

\section{Simple Models}
For many random effects models the random effects can be
specified very simply in the model formula.
As an example consider the \texttt{eortc} data set, which
is included with the package for illustration.
This is a simple simulated example, based on the results
of a breast cancer trial undertaken by the 
European Organization for Research and Treatment of Cancer.
There are 37 enrolling centers with enrollments ranging from
21 to 247 subjects.
We start by fitting a simple model with a random intercept
per center.
<<>>=
library(coxme)
stem(table(eortc$center))

efit1 <- coxph(Surv(y, uncens) ~ trt, eortc)
efit2 <- coxme(Surv(y, uncens) ~ trt + (1|center), eortc)
@ 
Random effects are specified in the formula by a parenthesised
expression which contains a vertical bar separating effects on
the left from grouping variables on the right.
In the case above we read it as an intercept (effect) per center (group).
<<>>=
print(efit2)
@ 
The components of the printout are
\begin{itemize}
  \item The total number of observations and the total number of events 
(deaths) in the data set.
  \item The computational effort, summarized as the number of iterations
for the optim routine and the underlying Newton-Raphson iterations used.
  \item The log partial likelihood for a model with no covariates or random
effects, the fitted partial likelihood, and the value with the random effects
integrated out.  We will normally be interested in the null and integrated
values.  (The log values are printed, but labeled as PL and IPL for
brevity).
  \item Likelihood ratio tests based on the integrated and penalized views
of the model, along with penalized values.  The AIC penalizes by twice the
effective degrees of freedom, and the BIC by $\log(d)$ times the effective
degrees of freedom, where $d$ is the number of events.
\item A summary of the fixed effects
\item A summary of the variances of the random effects
\end{itemize}

After integrating out the random effects, the log partial likelihood 
for the mixed effects model is 
\Sexpr{round(diff(efit2$loglik[1:2])*2, 1)}.
As a test of the random effects, we would normally compare this to 
\texttt{efit1}, the fit with no random effects which has a log
partial likelihood of \Sexpr{round(diff(efit1$loglik)*2,1)}
The estimated standard deviation between centers of .33 is
fairly substantial (more on this below).
The difference is $>100$ on one degree of freedom, which is highly
significant.

Another way to approach the fit is as a penalized model.
The coefficients are viewed as the solution to a penalized
problem $\log(PPL(\beta, b)) - \log(PL(\beta,b)) - p(\theta)$
where the penalty function $p(\theta) =  b'\Sigma^{-1}(\theta)b/2$.
In this case we use the ordinary $PL$ for assessment, but with
an effective degrees of freedom which lies somewhere between
the total number of coefficients $(\beta, b)$ of 38 and the
degrees of freedom for the fixed effects of 1. 
For this particular problem $\Sigma(\theta) = \theta I$,
a multiple of the identity matrix and our penalty is
$p(\theta) = -1/(2\theta) \sum{j=1}{37} b_j^2$. 
As the penalty goes to zero the fit will be equivalent
to treating factor(center) as a fixed effect with degrees
of freedom equal to 38, 
as the penalty becomes larger the effective degrees of 
freedom will decrease.
In this case a test for the random effects would be based
on (319.7 - 105.7) on 27.7 degrees of freedom.  
We do not normally use this test, but the adjusted degrees 
of freedom is useful in summarizing the fit.

The major components of the model can be extracted via the 
following functions:
\begin{center}
\begin{tabular}{r|ccc} 
  &\multicolumn{3}{c}{Method} \\
  &lme/lmer & coxme $<2.2$ & coxme $\ge 2.2$ \\ \hline
  fixed effects coefficients & fixef & fixef & fixef \\
  random effects coefficients& ranef &  & ranef \\
  fixed effects variance matrix& vcov&  & vcov \\
  random effects parameters ($\theta$)   & VarCorr & ranef & VarCorr 
\end{tabular}
\end{center}
Note that for version 2.2 and greater these follow the
same pattern as the linear mixed effects models in R;
prior to version 2.2 the ranef method was incorrectly assigned.

One feature of the mixed effects Cox model is that the
standard deviation of the random effect is directly
interpretable.  
The random effects $b_j$ for each center $j$ are in the 
risk score, a value of .33 for instance (one standard deviation
above the mean) corresponds to a relative risk of
$\exp(.33)= 1.39$, an almost 40\% higher risk of death for subjects
at that center.  
(In real data sets we would of course have adjusted for imbalances
 in patient risk factors between centers.)
The random effects coefficients can be retrieved using the
\texttt{ranef} function.
The code below shows center effects ranging from less than 1/2 to
over 1.6 times the average risk for the study.
Because there may be multiple random effects in a model the 
ranef function returns a list, with one element per random effect.
We are interested in the first element (only element in this case)
of the list.
<<>>=
stem(exp(ranef(efit2)[[1]]))
@ 

To look at random treatment effects within center we can add
a nested effect
<<>>=
efit3 <- coxme(Surv(y, uncens) ~ trt + (1 | center/trt), eortc)
efit3
@ 
This shows a further improvement in fit, but by much smaller
amount.

There is one important difference between how random effects in
coxme and in lmer are handled concerning intercepts.
Linear models have an implied intercept term.
Unless explicitly removed, the model \verb!lm(y ~x)! will
actually fit the model \verb!lm(y ~x +1)!.
This carries forward into the notation of lmer where a random
effect \verb!(age | group)! will automatically add the random
intercept and fit \verb!(1 + age | group)!.
Cox models do not have an intercept term, and an automatic
``1'' is not added to either fixed effects \emph{or} random
effects portions of the formula.


\section{The Minnesota Breast Cancer Family Study}
\subsection{Background}
  Details of the study can be found in Sellers \cite{Sellers99}
Briefly, in 1944 a family study of breast cancer was initiated at the
Dight institute for Human Genetics at the University of Minnesota
to examine the influence of heredity on the risk of breast cancer.
Probands were a consecutive series of all breast cancer patients
ascertained at the tumor clinic of the University hospital between
1944 and 1952.  A total of 544 families were studied, representing
data on 4418 family members.  Results of that early study
provided some of the first evidence that breast cancer clusters in
families \cite{Anderson58}.

Records of the research then sat dormant for 40 years, until a series of
follow-up studies was initiated in the late 1990s.  
Prevalent cases ($n=40$) and families where most of the relatives other than
the proband were deceased at baseline ($n=42$) were excluded.  Of the
remainder 30 families could be contacted and 6 refused, leaving 426
participating families.  After extending pedigrees to the current
generation, the final data set has 28081 subjects.  
The full set of subjects along with a few selected variables 
is included in the \texttt{kinship2} package as the 
\texttt{minnbreast} data set.
The analysis of this data set was the original genesis of the coxme function.

<<>>=
library(coxme)
library(kinship2)
options(show.signif.stars=FALSE)
makefig <- function(file) {
    pdf(paste(file, "pdf", sep='.'), width=7, height=5)
    par(mar=c(5.1, 4.1, .1, .1))
}

names(minnbreast)
with(minnbreast, table(sex, cancer, exclude=NULL))

mped <- with(minnbreast, pedigree(id, fatherid, motherid, sex,
                                  affected=cancer, famid=famid,
                                  status=proband))
makefig("cfig1")
plot(mped["8"])  #figure 1
dev.off()
@ 

\begin{figure}
  \myfig{cfig1}
  \caption{Pedigree for family 8 of the Minnesota Breast Cancer Study.
    Cancer cases are filled, the proband is marked with a slash}
  \label{cfig1}
\end{figure}

Figure \ref{cfig1} shows the pedigree for family 8.  The original
case (the proband) has a mother, daughter, and niece with breast cancer.
This is a high risk family.  There is also a brother-in-law with
prostate cancer. 

An aside. Our first action was to turn off one of the most egregious
abominations of statistical packages: using stars to train our users
that ``$<.05$'' is all that matters, when in fact that is one of the
most useless ways known to employ statistical methods.
I also like to place my figures within the document using the
latex \verb+\begin{figure}+ command, hence I use \texttt{fig=FALSE}
as a default Sweave option and generate the pdfs directly.

\subsection{Simple models}
The simplest model is to look for a random intercept per family.
<<>>=
minnfemale <- minnbreast[minnbreast$sex == 'F' & !is.na(minnbreast$sex),]
fit0 <- coxph(Surv(endage, cancer) ~ I(parity>0), minnfemale,
              subset=(proband==0))
summary(fit0)

fit1 <- coxme(Surv(endage, cancer) ~ I(parity>0) + (1|famid),
              minnfemale, subset=(proband==0))
print(fit1)
@ 
Note that we don't include the proband in the model.  Because they
are the index case that caused a family to be included, they have in
a probability of 1 for cancer and don't fit into the predictive
framework.

From the simple Cox model we see that ever having a child is protective,
reducing the risk of breast cancer by about 30\%. 
A mixed effects model has nearly the same estimate for parity.
The random family effect, an estimated intercept (excess risk) 
for each family, has a standard deviation of .41.  
We would expect about 15\% of the families to be 1 standard deviation
or more above the mean, and these families will have a breast
cancer risk that is exp(.41)=1.5 times the norm.  A similar fraction
have lower risk.
This is a modestly large familial effect.

The result of \texttt{fit1} is also known as a \emph{shared frailty} model.
The term originally arose in demography, where the excess risk $f_k=\exp(b_k)$ 
for family $k$ could be thought of as an underlying propensity for failure
or ``frailty'' for the subject. 
The statistics literature is divided on the use of $f$ or frailty as the
underlying random variable or using $b$.
The first leads to investigation of random effects
distributions that are $>0$ such as the gamma, positive stable, and log-normal.
We strongly prefer the latter form, however, first since it ties into the
very familiar notational structure of linear mixed effects models,
and second because the Gaussian forms a simpler framework for multiple 
and correlated random effects.  

\begin{figure}
  \myfig{cfig2}
  \caption{The number of breast cancers in each family versus the
    estimated familial risk}
  \label{cfig2}
\end{figure}

<<>>=
ncancer <- with(minnfemale, tapply(cancer, famid, sum, na.rm=T))
pyears <-  with(minnfemale, tapply(endage -18, famid, sum, na.rm=T))
count  <-  with(minnfemale, tapply(cancer, famid, 
                                   function(x) sum(!is.na(x))))
indx <- match(names(ranef(fit1)[[1]]), names(ncancer))                

makefig("cfig2")
plot(ncancer[indx], exp(ranef(fit1)[[1]]), log='y',
     xlab="Number of cancers per family",
     ylab="Estimated familial risk")
abline(h=1, lty=2)
text(c(8.1, 1.6), c(.85, 1.2), c("165", "72"))
dev.off()

indx <- match(c(72,165), names(ncancer))
temp <- cbind(ncancer, count, pyears, 100*ncancer/pyears)[indx,]
dimnames(temp) <- list(c(72, 165), 
                       c("Cancers", "N", "Years of FU", "Rate"))
print(round(temp,2))
@ 

Figure \ref{cfig2} shows number of cancers in each family versus the
estimated excess risk from the model.  
Family 72 has a high risk but only 2 cases; there are only 5 female
relatives in the family and the cancers occur at ages 32 and 36. 
Because the family is small the model has shrunk the estimated risk
closer to the overall mean.
Family 165 has 8 cancers but is has lower risk than the average
Minnesota subject due to the large family size.

\begin{figure}
  \myfig{cfig3}
  \caption{Profile likelihood for the per-family random effects
    model}
  \label{cfig3}
\end{figure}
There is not a good closed form formula for the variance of the
variance estimates themselves, i.e., one that is both reliable and
simple to compute. 
Valid confidence intervals can be obtained from a profile likelihood,
however.  We start by computing the likelihood over a range of 
values for the variance.
<<>>=
estvar <- seq(.2, .6, length=15)^2  #range of std values
loglik <- double(15)
for (i in 1:15) {
    tfit <- coxme(Surv(endage, cancer) ~ I(parity>0) + (1|famid),
                  data=minnfemale, subset=(proband==0),
                  vfixed=estvar[i])
    loglik[i] <- 2*diff(tfit$loglik)[1]
}
makefig("cfig3")
plot(sqrt(estvar), loglik, 
     xlab="Std of the random effect", ylab="2 * loglik")
abline(h=2*diff(fit1$loglik)[1] - qchisq(.95, 1), lty=2)
dev.off()
@ 
The 95\% profile likelihood confidence interval is the region where
the curve lies above the line, i.e., the set of values $x$ for
which a 1 degree of freedom likelihood ratio test would not
reject the hypothesis that the true standard deviation = $x$.
(It will sometimes take a couple of tries to guess an appropriate
range for the variance, for many data sets it will be considerably 
wider than this one).  
We can easily get a numeric estimate from the graph, giving
a confidence interval from .28 to .53.
<<>>=
temp <- 2*diff(fit1$loglik)[1] - loglik
approx(temp[1:8], sqrt(estvar[1:8]), 3.84)$y
approx(temp[9:15], sqrt(estvar[9:15]), 3.84)$y
@ 

\subsection{Correlated Random Effects}
The simple family intercept model has provided some insight, but it
has two serious flaws.
\begin{itemize}
  \item Subjects will be counted in a family's risk who should actually   %'
    be excluded.  In family 8 there are also 4 women who have married
    into the family; they are not shown on figure \ref{cfig1} since
    they have no children and hence have no \emph{genetic} relationship to
    family 8 at all.  Yet they are counted in the familial risk.
  \item More generally, the simple model cannot account 
    for degrees of association.  In
    figure \ref{cfig1} subject 162 is a marry-in and so does not share the
    familial burden of the proband/mother/daughter cancers.  Yet she is
    connected indirectly to the family through her high risk daughter.
    The largest families have over 200 individuals, some of whom are only
    distantly related while others are close.
\end{itemize}

We approach this by making use of the kinship matrix $K$. 
Formally, $K_{ij}$ is the probability that for any given gene, an
allele randomly chosen from subject $i$ and another randomly chosen from
subject $j$ will be identical by descent, that is, have been passed
down from a common ancestor.  Then $2K$ is a
measure of the expected fraction of shared genes: 1 on the diagonal,
1/2 for mother/daughter or sib/sib, 1/4 for grandparent/grandchild or
uncle/niece, etc.  If subject $i$ and $j$ are from different families
then $K_{ij}=0$ by definition, since they have no common ancestor.
The relevant Cox model is
\begin{align*}
  \lambda_i(t) &= \lambda_0(t) e^{X\beta + b_i} \\
  b_i &\sim N(0, \sigma^2 2K) 
\end{align*}
Each subject has an individual random genetic risk $b_i$, but those
risks are correlated according to the strength of relationship.
The model is easy to fit in \texttt{coxme}
<<>>=
kmat <- kinship(mped)
fit2 <- coxme(Surv(endage, cancer) ~ I(parity>0) + (1|id),
              data=minnfemale, varlist=coxmeMlist(2*kmat, rescale=F),
              subset=(proband==0))
print(fit2)
@ 

The estimated genetic effect is much larger, with a standard deviation
of almost 0.9.  This means that there are multiple subjects in the study
with quite large relative risks, exp(.9)= 2.5 fold greater than
the average Minnesotan.

Note that even though we are only interested in fitting the females,
creation of the kinship matrix \texttt{kmat} requires use of all the 
subjects in the pedigree.  
When the model is fit appropriate rows/columns of the matrix are
selected by matching its dimnames with the id variable.
In fitting the model we have to explicitly describe the variance
structure using one of a small set of variance functions.  The
coxmeMlist function accepts a set of matrices $V_1$, $V_2$, \ldots
as arguments and fits the an overall variance matrix
$\sigma_1^2 V_1 + \sigma_2^2 V_2 + \ldots$, solving for the optimal
values of $\sigma$.  
A major portion of the code in the coxmeMlist function 
is devoted to ensuring that each level of the grouping variable ---
\texttt{id} in this instance --- exists in the matrix, that all matrices
have the same row and column order, and other consistency checks.

Since $K_{ij}=0$ for any unrelated individuals, the kinship matrix is
mostly zeros.  Although it has a theoretical size of just under
800 million elements (\texttt{prod(dim(kmat))}) only the 500,000 non-zero
elements are stored (\texttt{length(kmat@x)}) using a sparse matrix form
based on the Matrix package.
To maximize storage efficiency families are clustered together in
adjacent rows.  This carries through to the calculations, and
thus it is the order of the elements in \texttt{kmat} which
determines the order of the random effects coefficients $b$ in the
fitted model.  Do not expect them to be sorted in the same way as
as they were in the data set, nor in the sorted order found for a 
factor variable in an ordinary Cox model.  
(For a simple model like \texttt{fit1} which does not
require sparse storage they will be in standard order, however.)

\subsection{Breast and prostate cancer}
An interesting genetic question is whether there might be a connection
between various cancers. That is, inherited traits that predispose
subjects to all or some subset of malignancies.
Grabrick et al \cite{Grabrick03} undertook a sub study within the
Minnesota Breast Family cohort of possible genetic connections between
breast and prostate cancer. A subset of 206 families was selected, and
all men in those families were invited to participate.  Living male members
were sent a questionnaire, for those deceased or unable to answer a shortened
form was sent to their spouse or close relative.

  More to do for this section
  
\section{Random slopes} 

 

\begin{thebibliography}{9}
    \bibitem{Anderson58}
      V E Anderson, H O Goodman, S C Reed. \emph{Variables related to
	human breast cancer}.  University of Minnesota Press, 1958.

  \bibitem{Grabrick03} D M Grabrick, J R Cerhan, R A Veirkant,
            T M Therneau, J C Cheville, D J Tindall, 
            and T A Sellers, 
            Evaluation of familial clustering of breast and prostate cancer
          in the Minnesota Breast Cancer Family Study,
    \emph{Cancer Detection and Prevention}, 27:30--36, 2003.

\bibitem{Ripatti02}
  S Ripatti and J Palmgren.  Estimation of multivariate frailty models
  using penalized partial likelihood.  Biometics 56:1016--1022, 2002.
     
\bibitem{Sellers99}
    T A Sellers, R A King, J R Cerhan, P L Chen, D M Grabrick, L H Kushi, 
    W S Oetting, R A Vierkant, C M Vachon, F J Couch, T M Therneau, 
    L C Hartman and V E Anderson.  Fifty year follow-up of cancer
    incidence in a historical cohort of Minnesota breast cancer families.
    \emph{Cancer Epid Biomarkers Prev} 8:1051--7, 1999.
    
\bibitem{Therneau00}
  T M Therneau and P M Grambsch, Modeling Surivival Data: Extending the Cox
 Model, Springer-Verlag, 2000.    
 \end{thebibliography}
\end{document}
