Estimates population-level ODE parameters for longitudinal biomarker trajectories: \(\ddot{m}(t) = \beta_1 m(t) + \beta_2 \dot{m}(t) + X\beta\)
Usage
MarginalODE(
formula,
data,
time = "time",
id = "id",
state = NULL,
control = list()
)Arguments
- formula
Response and covariates (e.g.,
biomarker ~ x1 + x2)- data
Data frame with longitudinal measurements
- time
Time variable name (default:
"time")- id
Subject identifier name (default:
"id")- state
Optional \(n \times 2\) matrix of initial conditions \([m(0), \dot{m}(0)]\). If
NULL, estimated from data.- control
List of control parameters. See
MarginalODE.control.
Examples
if (FALSE) { # \dontrun{
fit <- MarginalODE(
formula = observed ~ x1 + x2,
data = sim$data$longitudinal_data,
state = as.matrix(sim$data$state)
)
} # }