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\title{Coxme and the Laplace Approximation}
\author{Terry Therneau \\Mayo Clinic}
\begin{document}
\maketitle
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@
\section{Laplace approximation}
The coxme function fits the following mixed effects Cox 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 $\theta$.
The MLE for the variance of the random effects is based on
an integrated partial likelihood
\begin{equation}
IPL(\beta, \theta) = \frac{1}{(2\pi)^{q/2}|\Sigma(\theta)|^{1/2}}
\int PL(\beta, b) e^{-b'\Sigma^{-1}(\theta)b/2}\,db
\label{ipl}
\end{equation}
where $q$ is the dimension of the Gaussian density, i.e., the
number of random effects.
When the variance of the random effect is zero this collapses
to the ordinary Cox partial likelihood.
The result of a coxme fit prints out three log-likelihood terms:
the fit for a null model where $\beta$=0 and the variance of the
random effect is zero (and therefore $b=0$),
the log of the integrated value $IPL(\hat\beta, \hat\theta)$ and the
log partial likelihood $PL(\hat\beta, \hat b)$.
(For brevity ``log'' is not printed in their labels.)
However, the IPL \eqref{ipl} above is not a tractable integral.
The key step in its computation is
replacement of the log penalized partial
likelihood $LPPL$ with a second order Taylor series about its value
at the maximum of the function
\begin{align*}
PL(\beta, b) &= e^{\log(PL(\beta,b))} \equiv e^{LPL(\beta,b)} \\
LPPL(\beta, b, \theta) &= LPL(\beta, b) - (1/2) b'A^{-1}(\theta)b \\
&\approx LPPL(\hat\beta(\theta), \bhat(\theta)) -
(1/2) (\beta- \hat\beta(\theta),
b- \bhat(\theta))' H (\beta- \hat\beta(\theta), b-\bhat(\theta))
\end{align*}
where the Hessian $H$ is -1 times the matrix of second derivatives of the LPPL,
evaluated
at $(\hat \beta(\theta), \bhat(\theta))$.
When $\theta$ and hence $A(\theta)$ are fixed, the relevant values
of $\beta$ and $b$ that maximize the LPPL are easily computed
using essentially the same methods as an ordinary Cox model.
For the ML estimate we are only interested in the values at $\hat \beta$
so the last term collapses to
$(0,b- \bhat)' H (0, b- \bhat) = (b- \bhat)' H_{bb}(b- \bhat)$, where
$H_{bb}$ is the portion of the Hessian corresponding to the random effects.
When we replace the body of the integral in \eqref{ipl} with this
approximation, then result is an integral that we can solve
in closed form.
The result is what is printed as the IPL in coxme.
A key question, of course, is whether the result is a \emph{good}
approximation to the IPL.
The answer appears to be that it is, if there are a sufficient number of
observations that contribute to each random effect.
The definition of the word ``sufficient'' is not completely clear,
and the coxme routine includes a option \texttt{refine.n} which
does a monte carlo refinement of the final solution, allowing for
diagnosis of whether we are in a excellent (often), bad, or intermediate
case with respect to the approximation.
The remainder of this note gives further details.
\section{Computation}
The central computational strategy for \code{coxme} is an outer and
an inner loop. The outer loop searches over the parameters $\theta$
of the variance matrix for a maximum of the IPL.
For each trial value of $\theta$ in this search
\begin{enumerate}
\item Calculate $A(\theta)$ and $A^{-1}(\theta)$
\item Solve the penalized Cox model $LPL(\beta, b) - (1/2) b'A^{-1}b$ to
get the solution vector $(\hat\beta, \bhat)$, where $PL$ is the usual
Cox partial log-likelihood. The iterative Newton-Raphson solution
to this problem is the inner loop.
\item Use the Laplace approximation to compute the log IPL, using the
results of step 2.
\end{enumerate}
A necessary component of the solution in step 2 is calculation of
$H$ and its generalized Cholesky decomposition $H=LDL'$, where $D$ is %'
diagonal and $L$ is lower triangular with $L_{ii}=1$.
The determinant $|H|$ is the product of the diagonal
elements $D$.
The Laplace approximation in step 3 is particularly convenient
for this problem since all the components are already in hand.
\section{Refining the approximation}
\subsection{Random treatment effects}
As an example case, we
first look at a simple simulated data set with random institution and treatment
within institution effects.
<<>>=
library(coxme)
set.seed(1953) # an auspicious birth year :-)
mkdata <- function(n, beta=c(.4, .1), sitehaz=c(.5,1.5, 2,1)) {
nsite <- length(sitehaz)
site <- rep(1:nsite, each=n)
trt1 <- rep(0:1, length=n*nsite)
hazard <- sitehaz[site] + beta[1]*trt1 + beta[2]*trt1 * (site-mean(site))
stime <- rexp(n*nsite, exp(hazard))
q80 <- quantile(stime, .8)
data.frame(site=site,
trt = trt1,
futime= pmin(stime, q80),
status= ifelse(stime>q80, 0, 1),
hazard=hazard
)
}
trdata <- mkdata(150) #150 enrolled per site
fit1 <- coxme(Surv(futime, status) ~ trt + (1| site/trt), trdata)
print(fit1)
# Show the true and estimated per-site intercepts
true <- c(.5, 1.5, 2, 1) - mean(c(.5, 1.5, 2, 1))
bcoef <- ranef(fit1)[[2]]
temp <- rbind(true, bcoef)
dimnames(temp) <- list(c("True", "Estimated"), paste("Site",1:4))
round(temp,2)
@
The true site hazards have standard deviation
\code{sqrt(var(c(.5, 1.5, 2, 1)))} =
.65, the estimate from the fit is \Sexpr{round(sqrt(VarCorr(fit1)[[2]]),2)}.
In this case the fit has reconstructed the per site intercepts reasonably well.
\begin{figure}[tb]
\myfig{laplace-fig1}
\caption{The solid lines are profiles of $PPL(\hat\beta, b)$, dashed lines
are the Taylor series approximation.}
\label{fquad}
\end{figure}
Figure \ref{fquad} is a plot of profiles of the likelihood
for the four institution
effects. We vary $b$ for each institution while holding all of the other
coefficients and the variance fixed. This shows four ``slices'' through the
12 dimensional LPPL as a function of $b$.
The approximation is not perfect --- each LPPL slice is rotated just a
little from its quadratic approximation as we move away from the maximum.
But remember that we are computing an average of exp(LPPL),
so any part of the curves
more than 20 units below the max will hardly matter,
at least for this small number of dimensions.
Code to draw the figure is below.
A coxph model with only an offset term is a convenient way to compute
the partial likelihood for a fixed model.
Also note that random effects are coded using a full contrast matrix.
The institution by treatment effects generate 8 random terms $b_1$ to $b_8$
and the four per-institution intercepts $b_9$ to $b_{12}$.
Unlike fixed effects where one of the 4 intercepts would be eliminated
due to redundancy
(exactly how this is done depends on the contrasts option),
the random effects induce two sum constraints $\sum_1^8 b_i=0$ and
$\sum_9^{12} b_i=0$.
<>=
xx <- seq(-1, 1, length=101) #vary b from -1 to 1
profile <- matrix(0, nrow=101, ncol=8) #to store curves
bcoef <- unlist(ranef(fit1))
indx <- -1 + trdata$trt + 2*trdata$site #random treatment effect index
Ainv <- diag(1/rep(unlist(VarCorr(fit1)), c(8,4)))
for (i in 1:4) {
tcoef <- bcoef
for (j in 1:101) {
tcoef[i+8] <- xx[j] #reset single coef
eta <- fixef(fit1)*trdata$trt + tcoef[trdata$site+8] +
tcoef[indx]
tfit <- coxph(Surv(futime, status) ~ offset(eta), data= trdata)
profile[j,i] <- tfit$loglik - .5*tcoef%*% Ainv %*% tcoef
profile[j, i+4] <- fit1$loglik[3] -
.5*sum(((tcoef-bcoef) %*% fit1$hmat[1:12, 1:12])^2)
}
}
matplot(xx, profile-fit1$loglik[3], type='l', lty=c(1,1,1,1,2,2,2,2), col=1:4,
ylim=c(-40,0),
xlab="b", ylab="LPPL - max")
@
One returned component of coxme is \code{hmat}, which contains the
generalized Cholesky decomposition $LDL'$ of $H$,
based on the bdsmatrix library.
To take advantage of sparse matrices, the coxme code orders the coefficients as
$(b, \beta)$, so we want the upper left portion of $H$ in our code.
A product \verb!x %*% fit$hmat! returns $y = xLD^{1/2}$, then
$yy' = xHx'$ = \verb!sum(y^2)!.
\subsection{Control sampling}
To evaluate the integral numerically we use variance reduction methods.
Control sampling is based on the simple equation
\begin{equation*}
C = B + (C-B)
\end{equation*}
In this case $C$ is the desired integral, the right hand side of
equation \eqref{ipl}, $B$ is the Laplace approximation to the
integral, and we simulate $C-B$.
\begin{align}
C-B &= n(A) \int e^{LPL(\hat\beta, b) -b'A^{-1}b/2} -
e^{LPPL(\hat\beta, \hat b) - (b-\hat b)' H_{bb} (b-\hat b)/2} db
\label{control1} \\
& = n(A) e^k \int \frac{e^{LPPL(\hat\beta, b) -k} -
e^{LPPL(\hat\beta, \hat b) - (b-\hat b)' H_{bb} (b-\hat b)/2 -k}}
{g(b)} \, g(b) db
\label{control2}
\end{align}
In equation \eqref{control1} we expect the integrand to be close to
zero for all values of $b$.
Since the variance of our Monte Carlo result is the variance of this
integrand divided by the number of simulations, the Monte Carlo
result will also be precise.
A Monte Carlo evaluation with respect to the vague prior $db$ is not
possible, however, and equation \eqref{control2} rewrites this so that
we sample from a distribution $g(b)$.
The divisor $\exp(k)$ is chosen to keep the arguments of the
two exponentials in bounds and avoid underflow/overflow errors.
The choice of $g$ is important. We want to make sure that $g$
is never tiny when the numerator is near it's largest values, %'
as that would generate large values and erase much of the
good done by the control function.
Both exponentials reach their maximum at $\hat b$ so it seems
sensible to center $g$ there. The difference in the
numerator can be no bigger than the smaller of the two
exponentials, so a distribution that falls away a little
more slowly than the right hand quadratic term would
add the desired margin of safety.
A natural choice satisfying these two is a multivariate
t-distribution with variance matrix $H^{-1}_{22}$ and a modest
degrees of freedom.
Control sampling has been incorporated into coxme and is
invoked by the \texttt{refine.n} option.
The result for our sample data set is shown below.
<<>>=
fit1b <- coxme(Surv(futime, status) ~ trt + (1 | site/trt),
data=trdata, refine.n=500)
fit1b$refine
@
This verifies what the figure implied: the Laplace approximation
is excellent for this data set.
The eventual test for significance of the random effects will be based on
a chisquare distribution with 2 degrees of freedom, comparing the
IPL for the fitted coxme model to the PL from a fixed effects coxph
model with treatment alone. This suggests that any error in the
IPL that is less than 0.1 will be of little import.
\section{Further examples}
\subsection{EORTC}
As a larger example consider the eortc data set. This is
a simulated data set, but based on a large breast cancer clinical
trial. There are 37 enrollment centers, enrolling from 21 to 247
patients each.
<<>>=
efit2 <- coxme(Surv(y, uncens) ~ trt + (1|center), eortc,
refine.n=100)
efit2$refine
efit3 <- coxme(Surv(y, uncens) ~ trt + (1|center/trt), eortc,
refine.n=100)
efit3$refine
efit3
@
This behavior has been the norm for the author's experience %'
with coxme.
However, note that the total number of events
\Sexpr{efit3$n[2]} is much larger than the effective
degrees of freedom for the model of \Sexpr{round(efit3$df[2], 1)}.
We will return to this point.
\subsection{Ridge regression}
A classical method in linear models is ridge regression, which solves
the penalized regression problem
\begin{equation*}
\min_{\beta} ||y- X\beta||^2 + \lambda \sum_{j=1}^p \beta_j^2
\end{equation*}
This penalizes large values of the coefficients and can stabilize
problems with near collinear $X$ matrices.
As $\lambda$ goes to zero we approach the ordinary least squares result,
as $\lambda$ increases coefficients are shrunken towards zero.
The intercept term
$\beta_0$ is normally left out of the penalty.
The penalty can also be viewed as imposing a Gaussian prior
on the coefficients.
Thus, we can use coxme to perform ridge regression Cox models.
We will use an advanced lung cancer data set as our example, it is
part of the surival package.
<<>>=
lfit1 <- coxph(Surv(time, status) ~ age + ph.ecog + wt.loss, lung)
lfit2 <- coxme(Surv(time, status) ~ age + (ph.ecog |1) +
(wt.loss |1), data=lung, refine.n=100)
lfit2$refine
@
Again, the Laplace transform works very well.
By default the random coefficients $b$ are not included in the
printout, but they can be requested with the \texttt{rcoef}
option.
<<>>=
print(lfit2, rcoef=TRUE)
signif(rbind(coef(lfit1),
c(fixef(lfit2), unlist(ranef(lfit2)))),2)
@
Contrasting the coefficients between the shrunken and the regular
Cox models, the coeffienct for weight loss has been reduced over 10
fold while that for ECOG performace score has changed only a little.
Weight loss is a weak predictor in this data set and shrinking it
has only a small effect on the fit, whereas performance score
has a much stronger relationship to survival.
\subsection{Chronic Granulotomous Disease}
Children with chronic granulotomous disease are subject to
repeated infections due to an immune defect.
The CGD data set is based on a placeob controlled
randomized trial of gamma interferon for reduction of the
infection frequency,
during the course of the study enrolled subjects
experienced 0--7 infections each.
For further discussion of the data see Therneau and Grambsch~\cite{Therneau00}.
\begin{figure}[tb]
\myfig{laplace-cgd}
\caption{The two terms in the control function Monte Carlo
$\exp(e1) - \exp(e2)$.}
\label{fig:cgd}
\end{figure}
This data set is a much stiffer challenge for the
Laplace approximation since there are both a much
larger number of random effects (\Sexpr{length(unique(cgd$id))} subjects)
and we do not have ``a large number of events'' per random
effect. Over half of subjects, and hence their corresponding
coefficients $b_i$, have no events at all.
<>=
cfit <- coxme(Surv(tstart, tstop, status) ~ treat + age +
(1 | id), data=cgd, refine.n=500, refine.detail=TRUE)
cfit$refine
2*(diff(cfit$loglik[1:2]))
temp <- cfit$refine.detail
e1 <- (temp$loglik - temp$penalty1) - cfit$loglik[2]
e2 <- (cfit$loglik[3] - temp$penalty2) - cfit$loglik[2]
ssqrt <- function(x) sign(x)*sqrt(abs(x)) #signed square root
plot(ssqrt(e1), ssqrt(e2), xlab="sqrt(e1)", ylab="sqrt(e2)")
abline(0,1)
@
The Laplace approximation to the IPL is off by 1-2\% of the
difference between the null and fitted model.
In this particular case it is not a severe issue, the test
statistic for significance is $>9$ on 1 df, and even if it were not
``significant''
a correction for within-subject correlation is called for.
Figure \ref{fig:cgd} shows the two terms of equation \eqref{control2},
plotted on a square root scale to spread the data out.
In spite of pushing the approximation to it's limit, the %'
the quadratic approximation is still working remarkably well.
(The \texttt{refine.detail} option was intendend for debugging
the code, but the returned information can sometimes be
useful for digging deeper.)
\begin{figure}[tb]
\myfig{laplace-cgd2}
\caption{The Laplace approximation to the IPL for the CGD data
is in black for a range of trial variances,, along with
a Monte Carlo correction to this in red. The vertical
red lines are $\pm 2$ times the estimated Monte Carlostandard
standard error.}
\label{fig:cgd2}
\end{figure}
A more interesting question is what impact this error might
have on the \emph{estimate} of $\theta$. We can investigate
this by looking at a set of fixed variances. The result
is shown in figure \ref{fig:cgd2}.
What we see is that the error increases with the variance
of the random effect and the the overall impact is to
underestimate the variance: the red (true IPL) maximizes
to the right of the maximized Laplace value.
The increase in bias is not surprising: as the variance increases
the distance from
the origin over which we are expecting the approximation to hold also
increases. The second is a subject of further investigation.
The horizontal line is 3.84/2 units below the Laplace maximum,
its intersection with the curve describes a 95\% confidence interval
for the standard deviation of the random effect.
The code to create the figure is shown below.
<>=
ss <- seq(.3, 1.3, length=25)
tmat <- matrix(0, nrow=25, ncol=3)
for (i in 1:25) {
tfit <- coxme(Surv(tstart, tstop, status) ~ treat + age + (1|id),
cgd, vfixed=ss[i]^2, refine.n=1000)
tmat[i,] <- c(diff(tfit$loglik[1:2]), tfit$refine)
}
temp1 <- tmat[,1] + tmat[,2] #corrected IPL
temp2 <- tmat[,1] + tmat[,2] + cbind(-2*tmat[,3], 2*tmat[,3]) # .955 CI
matplot(ss, cbind(tmat[,1], temp1), pch='o', col=1:2,
ylim=range(tmat[,1], temp2),
xlab="Std of random effect",
ylab="IPL - Null")
segments(ss, temp2[,1], ss, temp2[,2], lty=2, col=2)
lines(smooth.spline(ss, temp1, df=5), col=2)
abline(h= diff(cfit$loglik[1:2]) - qchisq(.95, 1)/2, lty=2)
@
\subsection{Colon cancer data}
The colon cancer data set (from the survival package) gives
progression and death times of 929 subjects enrolled in a 3 arm
clinical trial.
A joint analysis of the two outcomes should adjust for fact that
subject observations are correlated: in fact they are extremely
correlated given the nature of the disease.
An estimating equation model is our first choice.
<<>>=
cfit1 <- coxph(Surv(time, status) ~ rx + nodes + extent +
strata(etype) + cluster(id), colon)
cfit1
@
The fitted model shows no difference between the levamisole
and observation arms, an important decrease in risk for
the combination therapy levamisole + 5FU, and, as expected,
large effects for the number of lymph nodes and the
extent of tumor invasion. The reduction in standard error between
the model based and robust variance is almost $\sqrt{2}$,
which is what we would get if the two outcomes were perfectly
redundant.
A per subject random effect is not sensible when there is
only 1 event per subject, which is what we effectively have.
Nevertheless, we will fit and examine the result. %'
<<>>=
cfit2 <- coxme(Surv(time, status) ~ rx + nodes + extent +
strata(etype) + (1|id), colon,
refine.n=500)
cfit2$refine
print(cfit2)
round(quantile(ranef(cfit2)[[1]], 0:8/8), 2)
@
The variance of the random effects is very large at 7.6.
Subjects have estimated random effects of $\exp(-6.1)= .002$ (nearly
immortal) to $\exp(8.8) > 6600$ (dies before getting out
of the building) which are biologically implausible.
The control based refinement was not able to reliably
estimate the bias -- for many values of the random number
seed it actually returns NA due to computations that go
out of range.
A mixed effects model has not been successful for this data
set.
\subsection{Genetic studies}
Data sets that contain genetic correlations was actually the
genesis of the coxme function, therefore the performace of the
Laplace in this case is of particular interest to us.
The story here is still being worked out and we expect to
have a more detailed description in future versions of
this vignette. In broad strokes, when the correlation is
based on a kinship matrix the Laplace appears to work
adequately when: family sizes are modest to large and
the standard deviation of the random effect is no greater
than .8-.9. Even though there is a random effect per subject
in such models, the correlation structure is such that each
random effect is ``linked'' to a sufficiently number of events.
More work with a range of data sets needs to be undertaken, however.
\section{Conclusions}
On many data sets the Lapalce works very well, on others
it is adequate, and there are a few where fails.
An example of the last is the colon cancer data set.
However, this is a data set
for which I have grave doubts about the applicability of a
a mixed effects model at all,
a reservation that extends to any data set where the effective degrees
of freedom approaches the total number of events.
For the case of generalized linear models,
Shun and McCullaugh \cite{Shun95} suggest that the ordinary
Laplace approximation will be sufficient when the degrees of
freedom for the random effect is $o(\sqrt[3]{n})$.
For survival models experience with other cases such as AIC
suggests that the appropriate $n$ for such calculations is
the number of deaths.
Hall et al \cite{Hall05} point out one of the reasons for
difficulty when there are a large number of random effects.
The average distance from the center for a multivariate Gaussian
in $d$ dimensions is $\sqrt{d}$.
For large $d$ the law of large numbers guarrantees that essentially
all the mass in the distribution is in a narrow annulus $\sqrt{d}$
units from the center.
Consequently, this is the distance at which we are demanding accuracy
from the quadratic approximation when we use the Laplace method.
For the colon data we have 911 random effects and a variance of 7.6.
The distance from the origin is over 83 units which is too much to
ask of a second order Taylor series.
For our examples we had the following for number of events and
effective degrees of freedom:
\begin{center}
\begin{tabular}{rrr}
& Events & EDF \\ \hline
Simulation & 480 & 4.1 \\
eortc & 2323 & 39.3 \\
Ridge regression & 151& 2 \\
CGD & 76& 29.3 \\
colon cancer & 897& 725.8
\end{tabular}
\end{center}
The success with the CGD data suggest that for a mixed effects Cox model
at least, the Shun and McCullaugh bounds may be overly conservative.
Checking the reliabilty of the Laplace through use of the refine.n option
is encouraged.
\begin{thebibliography}{9}
\bibitem{Therneau00} T. Therneau and P. Grambsch, Modeling Survival Data:
Extending the Cox Model, Springer-Verlag, 2000.
\bibitem{Shun95} Z. Shun and P. McCullaugh, The Laplace approximation for
high dimensional integrals, JRSSB 57:749--760, 1995.
\bibitem{Hall05} P. Hall and J.S. Marron and A Neeman, Geometric representation
of high dimension, low sample size data. JRSSB: 67, 427--444, 2005.
\end{thebibliography}
\end{document}