long_est {CIMPLE}R Documentation

Coefficient estimation in the longitudinal model

Description

This function offers a collection of methods of coefficient estimation in a longitudinal model with possibly informative observation time. These methods include Standard linear mixed-effect model (standard_LME), Linear mixed-effect model adjusted for the historical number of visits (VA_LME), Joint model of the visiting process and the longitudinal process accounting for measured confounders (JMVL_LY), Inverse-intensity-rate-ratio weighting approach (IIRR_weighting), Joint model of the visiting process and the longitudinal process with dependent latent variables (JMVL_Liang), Imputation-based approach with linear mixed-effect model (imputation_LME), and Joint model of the visiting process and the longitudinal process with a shared random intercept (JMVL_G).

Usage

long_est(
  long_data,
  method,
  id_var,
  outcome_var,
  LM_fixedEffect_variables = NULL,
  time = NULL,
  LM_randomEffect_variables = NULL,
  VPM_variables = NULL,
  imp_time_factor = NULL,
  optCtrl = list(method = "nlminb", kkt = FALSE, tol = 0.2, maxit = 20000),
  control = list(verbose = FALSE, tol = 0.001, GHk = 10, maxiter = 150),
  ...
)

Arguments

long_data

Longitudinal dataset

method

The following methods are available:

  • standard_LME: Standard linear mixed-effect model.

  • VA_LME: Linear mixed-effect model adjusted for the historical number of visits.

  • JMVL_LY: Joint model of the visiting process and the longitudinal process accounting for measured confounders.

  • IIRR_weighting: Inverse-intensity-rate-ratio weighting approach.

  • JMVL_Liang: Joint model of the visiting process and the longitudinal process with dependent latent variables.

  • imputation_LME: Imputation-based approach with linear mixed-effect model.

  • JMVL_G: Joint model of the visiting process and the longitudinal process with a shared random intercept.

id_var

Variable for the subject ID to indicate the grouping structure.

outcome_var

Variable name for the longitudinal outcome variable.

LM_fixedEffect_variables

Vector input of variable names with fixed effects in the longitudinal model. Variables should not contain time.

time

Variable for the observational time.

LM_randomEffect_variables

Vector input of variable names with random effects in the longitudinal model. This argument is NULL for methods including JMVL_LY, JMVL_G and IIRR_weighting.

VPM_variables

Vector input of variable names in the visiting process model.

imp_time_factor

Scale factor for the time variable. This argument is only needed in the imputation-based methods i.e., imputation_LME.

optCtrl

Control parameters for running the mixed-effect model. See the control argument in lme4::lmer().

control

Control parameters for the JMVL_G method:

  • verbose: TRUE or FALSE for outputting checkpoint after each iteration. Default is FALSE.

  • tol: Tolerance for convergence.

  • GHk: Number of gaussian-hermite quadrature points. Default is 10.

  • maxiter: Maximum number of iteration. Default is 150.

...

Additional arguments to nleqslv::nleqslv().

Value

beta_hat: Estimated coefficients in the longitudinal model.

Other output in each method:

References

Buzkova, P. and Lumley, T. (2007). Longitudinal data analysis for generalized linear models with follow-up dependent on outcome-related variables. Canadian Journal of Statistics, 35(4):485–500.

Gasparini, A., Abrams, K. R., Barrett, J. K., Major, R. W., Sweeting, M. J., Brunskill, N. J., and Crowther, M. J. (2020). Mixed-effects models for health care longitudinal data with an informative visiting process: A monte carlo simulation study. Statistica Neerlandica, 74(1):5–23.

Liang, Y., Lu, W., and Ying, Z. (2009). Joint modeling and analysis of longitudinal data with informative observation times. Biometrics, 65(2):377–384.

Lin, D. Y. and Ying, Z. (2001). Semiparametric and nonparametric regression analysis of longitudinal data. Journal of the American Statistical Association, 96(453):103–126.

Examples

# Setup arguments
train_data

time_var = "time"
id_var = "id"
outcome_var = "Y"
VPM_variables = c("Z", "X")
LM_fixedEffect_variables = c("Z", "X")
LM_randomEffect_variables = "Z"

# Run the standard LME model
fit_standardLME = long_est(long_data=train_data,
                           method="standard_LME",
                           id_var=id_var,
                           outcome_var=outcome_var,
                           LM_fixedEffect_variables = LM_fixedEffect_variables,
                           time = time_var,
                           LM_randomEffect_variables = LM_randomEffect_variables,
                           VPM_variables = VPM_variables)
# Return the coefficient estimates
fit_standardLME$beta_hat

# Run the VA_LME model
fit_VALME = long_est(long_data=train_data,
                     method="VA_LME",
                     id_var=id_var,
                     outcome_var=outcome_var,
                     LM_fixedEffect_variables = LM_fixedEffect_variables,
                     time = time_var,
                     LM_randomEffect_variables = LM_randomEffect_variables,
                     VPM_variables = VPM_variables)
# Return the coefficient estimates
fit_VALME$beta_hat

[Package CIMPLE version 0.1.0 Index]