Last Value Carried Forward Method

Description

A simple approach to evaluate the effects of longitudinal covariates on the occurrence of events when the time-dependent covariates are measured intermittently. Regression parameter are estimated using last value carried forward imputation of missing values.

Usage

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lastValue(X, Z, tau, tol = 0.001, maxiter = 100 )

Arguments

X

an object of class data.frame. The structure of the data.frame must be {patient ID, event time, event indicator}. Patient IDs must be of class integer or be able to be coerced to class integer without loss of information. Missing values must be indicated as NA. The event indicator is 1 if the event occurred; 0 if censored.

Z

an object of class data.frame. The structure of the data.frame must be {patient ID, time of measurement, measurement(s)}. Patient IDs must be of class integer or be able to be coerced to class integer without loss of information. Missing values must be indicated as NA.

tau

an object of class numeric. The desired time point.

tol

An object of class numeric. The minimum change in the regression parameters deemed to indicate convergence of the Newton-Raphson method.

maxiter

An object of class numeric. The maximum number of iterations used to estimate regression parameters.

Value

A list is returned.

betaHat

The estimated model coefficients.

stdErr

The standard error for each coefficient.

zValue

The estimated z-value for each coefficient.

pValue

The p-value for each coefficient.

Author(s)

Hongyuan Cao, Mathew M. Churpek, Donglin Zeng, Jason P. Fine, and Shannon T. Holloway

References

Cao, H., Churpek, M. M., Zeng, D., and Fine, J. P. (2015). Journal of the American Statistical Association, in press.

See Also

fullKernel, halfKernel, nearValue

Examples

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  data(SurvLongData)
  # A truncated dataset to keep example run time brief
  exp <- lastValue(X = X[1:200,], Z = Z, tau = 1.0)