surv_est {CIMPLE} | R Documentation |
Coefficient estimation in the survival model with longitudinal measurements.
Description
This function offers a collection of methods of coefficient estimation in a
survival model with a longitudinally measured predictor. These methods
include Cox proportional hazard model with time-varying covariates (cox
),
Joint modeling the longitudinal and disease diagnosis processes (JMLD
), Joint
modeling the longitudinal and disease diagnosis processes with an adjustment
for the historical number of visits in the longitudinal model (VA_JMLD
), Cox
proportional hazard model with time-varying covariates after imputation
(Imputation_Cox
), Cox proportional hazard model with time-varying covariates
after imputation with an adjustment for the historical number of visits in
the longitudinal model (VAImputation_Cox
).
Usage
surv_est(
long_data,
surv_data,
method,
id_var,
time = NULL,
survTime = NULL,
survEvent = NULL,
LM_fixedEffect_variables = NULL,
LM_randomEffect_variables = NULL,
SM_timeVarying_variables = NULL,
SM_timeInvariant_variables = NULL,
imp_time_factor = NULL
)
Arguments
long_data |
Longitudinal dataset. |
surv_data |
Survival dataset. |
method |
The following methods are available:
|
id_var |
Variable for the subject ID to indicate the grouping structure. |
time |
Variable for the observational time. |
survTime |
Variable for the survival time. |
survEvent |
Variable for the survival event. |
LM_fixedEffect_variables |
Vector input of variable names with fixed effects in the longitudinal model. Variables should not contain time. |
LM_randomEffect_variables |
Vector input of variable names with random effects in the longitudinal model. |
SM_timeVarying_variables |
Vector input of variable names for time-varying variables in the survival model. |
SM_timeInvariant_variables |
Vector input of variable names for time-invariant variables in the survival model. |
imp_time_factor |
Scale factor for the time variable. This argument is
only needed in the imputation-based methods, e.g., |
Value
alpha_hat
: Estimated coefficients for the survival model.
Other output in each method:
-
JMLD
:-
beta_hat
: Estimated coefficients for the longitudinal model.
-
-
VA_JMLD
:-
beta_hat
: Estimated coefficients for the longitudinal model.
-
References
Rizopoulos, D. (2010). Jm: An r package for the joint modelling of longitudinal and time-to-event data. Journal of statistical software, 35:1–33.
Rizopoulos, D. (2012b). Joint models for longitudinal and time-to-event data: With applications in R. CRC press.
Examples
# Setup arguments
id_var = "id"
time = "time"
survTime = "D"
survEvent = "d"
LM_fixedEffect_variables = c("Age","Sex","SNP")
LM_randomEffect_variables = c("SNP")
SM_timeVarying_variables = c("Y")
SM_timeInvariant_variables = c("Age","Sex","SNP")
imp_time_factor = 1
# Run the cox model
fit_cox = surv_est(surv_data = surv_data,
long_data = long_data,
method = "cox",
id_var = id_var,
time = time,
survTime = survTime,
survEvent = survEvent,
LM_fixedEffect_variables = LM_fixedEffect_variables,
LM_randomEffect_variables = LM_randomEffect_variables,
SM_timeVarying_variables = SM_timeVarying_variables,
SM_timeInvariant_variables = SM_timeInvariant_variables,
imp_time_factor = imp_time_factor)
# Return the coefficient estimates
fit_cox$alpha_hat