mpl {bhm} | R Documentation |
Joint models for clustered data with binary and survival outcomes.
Description
{mpl} is a function to fit a joint model for clustered binary and survival data using maximum penalized likelihood (MPL) method with Jackknife variance.
Usage
mpl(formula, ...)
## S3 method for class 'formula'
mpl(formula, formula.glm, formula.cluster, data, weights=NULL,
subset = NULL, max.iter=100, tol = 0.005, jackknife=TRUE, ...)
#
# Use:
#
# fit = mpl(Surv(time, status)~w+z, y~x1+x2, ~cluster, data=data)
#
Arguments
formula |
an object of class "formula"(or one that can be coerced to that class): a symbolic description of the Cox proportiobal hazards model to be fitted for survival data. |
formula.glm |
an object of class "formula"(or one that can be coerced to that class): a symbolic description of the generalized linear model to be fitted for binary data. |
formula.cluster |
an object of class "formula"(or one that can be coerced to that class): a symbolic description of the cluster variable. |
data |
an optional data frame, list or environment (or object coercible by 'as.data.frame' to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the enviro nment from which mpl is called. |
weights |
an optional vector of weights to be used in the fitting process. Should be 'NULL' or a numeric vector. If non-NULL, weights options in the glm model and the coxph model will be assinged with the supplied weights. |
subset |
only a subset of data will be used for model fitting. |
max.iter |
Maximum number of iterations, default is max.iter = 100 |
tol |
Tolrance for convergence, default is tol = 0.005 |
jackknife |
Jackknife method for variance, default is jackknife = TRUE |
... |
additional arguments to be passed to the low level regression fitting functions (see below). |
Details
mpl(Surv(time, event)~w+z, y~x1+x2, ~cluster) will fit penalized likelihood for binary and survival data with cluster effect. Function print(x) can be used to print a summary of mpl results.
Value
mpl returns an object of class inheriting from "mpl". When jackknife = TRUE, an object of class "mpl" is a list containing the following components:
theta |
the maximum estimate of the regression coefficients and varaince component |
OR_HR |
Odds ratios (OR) and hazard ratios (HR) for binary and survival outcomes, respectively |
ase |
Asymptotic standard error for theta, which is usually understimated |
jse |
Jackknife standard error of theta based on resampling, this is considered to be more robust |
Note
Based on code from J. Wang.
Author(s)
Bingshu E. Chen (bingshu.chen@queensu.ca)
References
Chen, B. E. and Wang, J. (2020). Joint modelling of binary response and survival for clustered data in clinical trials. Statistics in Medicine. Vol 39. 326-339.
See Also
Examples
##
### No run
#
# fit = mpl(Surv(time, event)~trt+ki67, resp~trt+age, ~center.id)
#