joineRML (version 0.4.2)

mjoint: Fit a joint model to time-to-event data and multivariate longitudinal data

Description

This function fits the joint model proposed by Henderson et al. (2000), but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a (quasi-) Monte Carlo Expectation Maximization (MCEM) algorithm.

Usage

mjoint(formLongFixed, formLongRandom, formSurv, data, survData = NULL,
  timeVar, inits = NULL, verbose = FALSE, pfs = TRUE, control = list(),
  ...)

Arguments

formLongFixed

a list of formulae for the fixed effects component of each longitudinal outcome. The left hand-hand side defines the response, and the right-hand side specifies the fixed effect terms. If a single formula is given (either as a list of length 1 or a formula), then it is assumed that a standard univariate joint model is being fitted.

formLongRandom

a list of one-sided formulae specifying the model for the random effects effects of each longitudinal outcome. The length of the list must be equal to formLongFixed.

formSurv

a formula specifying the proportional hazards regression model (not including the latent association structure). See coxph for examples.

data

a list of data.frame objects for each longitudinal outcome in which to interpret the variables named in the formLongFixed and formLongRandom. The list structure enables one to include multiple longitudinal outcomes with different measurement protocols. If the multiple longitudinal outcomes are measured at the same time points for each patient, then a data.frame object can be given instead of a list. It is assumed that each data frame is in long format. tibble objects are automatically converted to plain data.frame objects.

survData

a data.frame in which to interpret the variables named in the formSurv. This is optional, and if not given, the required data is searched for in data. Default is survData=NULL.

timeVar

a character string indicating the time variable in the linear mixed effects model. If there are multiple longitudinal outcomes and the time variable is labelled differently in each model, then a character string vector can be given instead.

inits

a list of initial values for some or all of the parameters estimated in the model. Default is NULL, with initial values estimated using separate multivariate linear mixed effects and Cox proportional hazard regression models.

verbose

logical: if TRUE, the parameter estimates and other convergence statistics are value are printed at each iteration of the MCEM algorithm. Default is FALSE.

pfs

logical: if TRUE, then assuming the MCEM algorithm has converged, post-fit statistics including the posterior means and variances of the random effects, and the approximate standard errors are calculated and returned as part of the model object. Default is TRUE. If FALSE, then these additional calculations are not performed, which can reduce the overall computational time. This option is intended to be used with computationally intensive routines such as simulation and bootstrap standard error estimation where these calculations are not required.

control

a list of control values with components:

nMC

integer: the initial number of Monte Carlo samples to be used for integration in the burn-in phase of the MCEM. Default is nMC=100K.

nMCscale

integer: the scale factor for the increase in Monte Carlo size when Monte Carlo has not reduced from the previous iteration. Default is nMCscale=5.

nMCmax

integer: the maximum number of Monte Carlo samples that the algorithm is allowed to reach. Default is nMCmax=20000.

burnin

integer: the number of iterations for 'burn-in' phase of the optimization algorithm. It is computationally inefficient to use a large number of Monte Carlo samples early on until one is approximately near the maximum likelihood estimate. Default is burnin=100K for type='antithetic' or type='montecarlo' and burnin=5 for type='sobol' or type='halton'. For standard methods, such a large burn-in will generally be unnecessary and can be reduced on an application-specific basis.

mcmaxIter

integer: the maximum number of MCEM algorithm iterations allowed. Default is mcmaxIter=burnin+200.

convCrit

character string: the convergence criterion to be used. See Details.

gammaOpt

character string: by default (gammaOpt='NR'), \(\gamma\) is updated using a one-step Newton-Raphson iteration, with the Hessian matrix calculated exactly. If gammaOpt='GN', a Gauss-Newton algorithm-type iteration is implemented, where the Hessian matrix is approximated based on calculations similar to those used for calculating the empirical information matrix? If it is used, then the step-length is adjusted by a nominal scaling parameter of 0.5 in order to reduce the chance of over-shooting the maximizer.

tol0

numeric: tolerance value for convergence in the parameters; see Details. Default is tol0=1e-03.

tol1

numeric: tolerance value for convergence in the parameters; see Details. Default is tol1=1e-03.

tol2

numeric: tolerance value for convergence in the parameters; see Details. Default is tol2=5e-03 for type='antithetic' or type='montecarlo' and tol2=1e-03 for type='sobol' or type='halton'.

tol.em

numeric: tolerance value for convergence in the multivariate linear mixed model (MV-LMM). When \(K > 1\), the optimal initial parameters are those from the MV-LMM, which is estimated using a separate EM algorithm. Since both the E- and M-steps are available in closed-form, this algorithm convergences relatively rapidly with a high precision. Default is min(1e-04, tol2).

rav

numeric: threshold when using convCrit='sas' that applies absolute change (when \(<\)rav) or relative change (when \(\ge\)rav) criterion; see Details. Default is 0.1, which is an order of magnitude higher than the SAS implementation.

type

character: type of Monte Carlo integration method to use. Options are

type='montecarlo'

Vanilla Monte Carlo sampling.

type='antithetic'

Variance reduction method using antithetic simulation. This is the default option.

type='sobol'

Quasi-Monte Carlo with a low deterministic Sobol sequence with Owen-type scrambling.

type='halton'

Quasi-Monte Carlo with a low deterministic Halton sequence.

...

options passed to the control argument.

Value

An object of class mjoint. See mjoint.object for details.

Convergence criteria

The routine internally scales and centers data to avoid overflow in the argument to the exponential function. These actions do not change the result, but lead to more numerical stability. Several convergence criteria are available:

abs

the maximum absolute parameter change is \(<\)tol0. The baseline hazard parameters are not included in this convergence statistic.

rel

the maximum (absolute) relative parameter change is \(<\)tol2. A small value (tol1) is added to the denominator of the relative change statistic to avoid numerical problems when the parameters are close to zero.

either

either the abs or rel criteria are satisfied.

sas

if \(| \theta_p | < \)rav, then the abs criteria is applied for the l-th parameter; otherwise, rel is applied. This is the approach used in the SAS EM algorithm program for missing data.

Due to the Monte Caro error, the algorithm could spuriously declare convergence. Therefore, we require convergence to be satisfied for 3 consecutive iterations. The algorithm starts with a low number of Monte Carlo samples in the 'burn-in' phase, as it would be computationally inefficient to use a large sample whilst far away from the true maximizer. After the algorithm moves out of this adaptive phase, it uses an automated criterion based on the coefficient of variation of the relative parameter change of the last 3 iterations to decide whether to increase the Monte Carlo sample size. See the technical vignette and Ripatti et al. (2002) for further details.

Standard error estimation

Approximate standard errors (SEs) are calculated (if pfs=TRUE). These are based on the empirical observed information function (McLachlan & Krishnan, 2008). Through simulation studies, we have found that this approximation does not work particularly well for \(n < 100\) (where n is the number of subjects). In these cases, one would need to appeal to the bootstrap SE estimation approach. However, in practice, the reliability of the approximate SEs will depend of a multitude of factors, including but not limited to, the average number of repeated measurements per subject, the total number of events, and the convergence of the MCEM algorithm.

Bootstrap SEs are also available, however they are not calculated using the mjoint function due to the intense computational time. Instead, a separate function is available: bootSE, which takes the fitted joint model as its main argument. Given a fitted joint model (of class mjoint) and a bootstrap fit object (of class bootSE), the SEs reported in the model can be updated by running summary(fit_obj,boot_obj). For details, consult the bootSE documentation.

Details

Function mjoint fits joint models for time-to-event and multivariate longitudinal data. A review of relevant statistical methodology for joint models of multivariate data is given in Hickey et al. (2016). This is a direct extension of the models developed in the seminal works of Wulfsohn and Tsiatis (1997) and Henderson et al. (2000), with the calculations based largely on Lin et al. (2002) who also extended the model to multivariate joint data. A more detailed explanation of the model formulation is given in the technical vignette. Each longitudinal outcome is modelled according to a linear mixed model (LMM), akin to the models fit by lme, with independent and identically distributed Gaussian errors. The latent term in each model (specified by formLongRandom) is a linear combination of subject-specific zero-mean Gaussian random effects with outcome-specific variance components. We denote these as \(W_{i1}(t, b_{i1})\), \(W_{i2}(t, b_{i2})\), \(\dots\), \(W_{iK}(t, b_{iK})\), for the K-outcomes. Usually, \(W(t, b)\) will be specified as either \(b_0\) (a random-intercepts model) or \(b_0 + b_1t\) (a random-intercepts and random-slopes model); however, more general structures are allowed The time-to-event model is modelled as per the usual Cox model formulation, with an additional (possibly) time-varying term given by

$$\gamma_{y1} W_{i1}(t, b_{i1}) + \gamma_{y2} W_{i2}(t, b_{i2}) + \dots + \gamma_{yK} W_{iK}(t, b_{iK}),$$

where \(\gamma_y\) is a parameter vector of proportional latent association parameters of length K for estimation.

The optimization routine is based on a Monte Carlo Expectation Maximization algorithm (MCEM) algorithm, as described by Wei and Tanner (1990). With options for using antithetic simulation for variance reduction in the Monte Carlo integration, or quasi-Monte Carlo based on low order deterministic sequences.

References

Henderson R, Diggle PJ, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics. 2000; 1(4): 465-480.

Hickey GL, Philipson P, Jorgensen A, Kolamunnage-Dona R. Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues. BMC Med Res Methodol. 2016; 16(1): 117.

Lin H, McCulloch CE, Mayne ST. Maximum likelihood estimation in the joint analysis of time-to-event and multiple longitudinal variables. Stat Med. 2002; 21: 2369-2382.

McLachlan GJ, Krishnan T. The EM Algorithm and Extensions. Second Edition. Wiley-Interscience; 2008.

Ripatti S, Larsen K, Palmgren J. Maximum likelihood inference for multivariate frailty models using an automated Monte Carlo EM algorithm. Lifetime Data Anal. 2002; 8: 349-360.

Wei GC, Tanner MA. A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. J Am Stat Assoc. 1990; 85(411): 699-704.

Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics. 1997; 53(1): 330-339.

See Also

mjoint.object, bootSE, plot.mjoint, summary.mjoint, getVarCov.mjoint, simData.

Examples

# NOT RUN {
# Fit a classical univariate joint model with a single longitudinal outcome
# and a single time-to-event outcome

data(heart.valve)
hvd <- heart.valve[!is.na(heart.valve$log.grad) & !is.na(heart.valve$log.lvmi), ]

set.seed(1)
fit1 <- mjoint(formLongFixed = log.lvmi ~ time + age,
    formLongRandom = ~ time | num,
    formSurv = Surv(fuyrs, status) ~ age,
    data = hvd,
    timeVar = "time",
    control = list(nMCscale = 2, burnin = 5)) # controls for illustration only
summary(fit1)

# }
# NOT RUN {
# Fit a joint model with bivariate longitudinal outcomes

data(heart.valve)
hvd <- heart.valve[!is.na(heart.valve$log.grad) & !is.na(heart.valve$log.lvmi), ]

fit2 <- mjoint(
    formLongFixed = list("grad" = log.grad ~ time + sex + hs,
                         "lvmi" = log.lvmi ~ time + sex),
    formLongRandom = list("grad" = ~ 1 | num,
                          "lvmi" = ~ time | num),
    formSurv = Surv(fuyrs, status) ~ age,
    data = list(hvd, hvd),
    inits = list("gamma" = c(0.11, 1.51, 0.80)),
    timeVar = "time",
    verbose = TRUE)
summary(fit2)
# }
# NOT RUN {
# }
# NOT RUN {
# Fit a univariate joint model and compare to the joineR package

data(pbc2)
pbc2$log.b <- log(pbc2$serBilir)

# joineRML package
fit.joineRML <- mjoint(
    formLongFixed = list("log.bil" = log.b ~ year),
    formLongRandom = list("log.bil" = ~ 1 | id),
    formSurv = Surv(years, status2) ~ age,
    data = pbc2,
    timeVar = "year")
summary(fit.joineRML)

# joineR package
pbc.surv <- UniqueVariables(pbc2, var.col = c("years", "status2"),
                            id.col = "id")
pbc.long <- pbc2[, c("id", "year", "log.b")]
pbc.cov <- UniqueVariables(pbc2, "age", id.col = "id")
pbc.jd <- jointdata(longitudinal = pbc.long, baseline = pbc.cov,
                    survival = pbc.surv, id.col = "id", time.col = "year")
fit.joineR <- joint(data = pbc.jd,
    long.formula = log.b ~ 1 + year,
    surv.formula = Surv(years, status2) ~ age,
    model = "int")
summary(fit.joineR)
# }