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:
|
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 |
Variable for the observational time. |
LM_randomEffect_variables |
Vector input of variable names with random
effects in the longitudinal model. This argument is |
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., |
optCtrl |
Control parameters for running the mixed-effect model. See
the |
control |
Control parameters for the
|
... |
Additional arguments to |
Value
beta_hat
: Estimated coefficients in the longitudinal model.
Other output in each method:
-
standard_LME
:-
beta_sd
: Standard deviation of the estimated coefficients.
-
-
VA_LME
:-
beta_sd
: Standard deviation of the estimated coefficients.
-
-
JMVL_LY
:-
gamma_hat
: Estimated coefficients in the visiting process model.
-
-
IIRR_weighting
:-
gamma_hat
: Estimated coefficients in the visiting process model.
-
-
JMVL_Liang
:-
gamma_hat
: Estimated coefficients in the visiting process model.
-
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