{mpl} is a function to fit a joint model for clustered binary and survival data using maximum penalized likelihood (MPL) method with Jackknife variance.
mpl(formula, ...)# S3 method for formula
mpl(formula, formula.glm, formula.cluster, data, weights=NULL,
subset = NULL, max.iter=100, tol = 0.005, jackknife=TRUE, ...)
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# Use:
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# fit = mpl(Surv(time, status)~w+z, y~x1+x2, ~cluster, data=data)
#
mpl returns an object of class inheriting from "mpl". When jackknife = TRUE, an object of class "mpl" is a list containing the following components:
the maximum estimate of the regression coefficients and varaince component
Odds ratios (OR) and hazard ratios (HR) for binary and survival outcomes, respectively
Asymptotic standard error for theta, which is usually understimated
Jackknife standard error of theta based on resampling, this is considered to be more robust
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.
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.
an object of class "formula"(or one that can be coerced to that class): a symbolic description of the cluster variable.
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.
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.
only a subset of data will be used for model fitting.
Maximum number of iterations, default is max.iter = 100
Tolrance for convergence, default is tol = 0.005
Jackknife method for variance, default is jackknife = TRUE
additional arguments to be passed to the low level regression fitting functions (see below).
Bingshu E. Chen (bingshu.chen@queensu.ca)
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.
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.
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### No run
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# fit = mpl(Surv(time, event)~trt+ki67, resp~trt+age, ~center.id)
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