joint                 package:joineR                 R Documentation

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_e_r_r_o_r

_D_e_s_c_r_i_p_t_i_o_n:

     This generic function fits a joint model with random latent
     association, building on the formulation described in Wulfsohn and
     Tsiatis (1997) while allowing for the presence of longitudinal and
     survival covariates, and three choices for the latent process. The
     link between the longitudinal and survival processes is assumed to
     be proportional.

_U_s_a_g_e:

     joint(longdat, survdat, longsep = FALSE, survsep = FALSE, gpt = 3,
     max.it = 200,
     tol = 0.001, lgpt = 10, model=c("intslope","int","quad"))

_A_r_g_u_m_e_n_t_s:

 longdat: a matrix containing both the longitudinal data and the
          longitudinal design matrix. The matrix will contain one row
          per measurement per subject. The matrix is required to have
          the column form: subject index, longitudinal response,
          longitudinal measurement time, longitudinal design matrix.

 survdat: matrix containing both the survival data and the survival
          design matrix and must contain one row per subject. The
          matrix is required to be set up in the following column
          order: subject index, survival time, censoring indicator,
          survival design matrix.

 longsep: if TRUE, parameter estimates and log-likelihood from a
          separate analysis of the longitudinal data (see the 'lme'
          function in the package nlme for details) are returned.

 survsep: if TRUE, parameter estimates and log-likelihood from a
          separate analysis of the survival data using the Cox
          proportional hazards model are returned (see 'coxph' in the
          survival package for details).

     gpt: the number of quadrature points across which the integration
          with respect to the random effects will be performed.
          Defaults to gpt=3 which produces stable estimates in most
          datasets.

  max.it: the maximum number of iterations which the function will
          perform. Defaults to max.it=200, though more iterations may
          be necessary for large, complex data.

     tol: the tolerance level before convergence of the algorithm is
          deemed to have occurred. Default value is tol=0.001.

    lgpt: the number of quadrature points which the log-likelihood is
          evaluated over following a model fit. This defaults to
          lgpt=10, though lgpt=3 is often sufficient.

   model: a character string specifying the type of latent association.
          This defaults to the intercept and slope version as seen in
          Wulfsohn and Tsiatis. For association via the random
          intercept only, choose model="int", whereas for a quadratic
          association, use model="quad". Computing times are
          commensurate with the type of association structure chosen.

_D_e_t_a_i_l_s:

     The function uses an EM algorithm to estimate parameters in the
     joint model. Starting values are provided by calls to standard R
     functions 'lme' and 'coxph' for the longitudinal and survival
     components respectively.

_V_a_l_u_e:

     A list containing the parameter estimates from the joint model
     and, if required, from either or both of the separate analyses.
     The combined log-likelihood from a separate analysis and the
     log-likelihood from the joint model are also produced as part of
     the fit.

_N_o_t_e:

     Both 'longsep' and 'survsep' ignore any latent association between
     the longitudinal and survival processes but output can be used to
     compare with the results from the joint model. If interest is
     solely in the individual processes then the user should instead
     make use of the functions 'lme' and 'coxph' mentioned above.
     Furthermore, if interest is in the separate effect of each random
     effect (this is for intercept and slope or quadratic models only)
     upon the survival data, the user should deploy function
     'jointsep', also in this package.

_A_u_t_h_o_r(_s):

     Pete Philipson (p.m.philipson@ncl.ac.uk)

_R_e_f_e_r_e_n_c_e_s:

     The general approach and model formulation is described by
     Wulfsohn, M. S. and Tsiatis, A. A.

     Wulfsohn, M. S. and Tsiatis, A. A. (1997) "A Joint Model for
     Survival and Longitudinal Data Measured with Error", Biometrics,
     53, 330-339.

_S_e_e _A_l_s_o:

     'lme','coxph','jointsep','jointcp','jointplot'.

_E_x_a_m_p_l_e_s:

     data(heartlong,heartsurv)
     fit1=joint(heartlong,heartsurv,model="int")

