plot.predMexhaz
(Package: mexhaz) :
Plot method for a predMexhaz object
Function for plotting the predicted (excess) hazard or (net) survival based on a predMexhaz object.
● Data Source:
CranContrib
● Keywords:
● Alias: plot.predMexhaz
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mexhaz
(Package: mexhaz) :
mexhaz function
Fit an (excess) hazard regression model using different shapes for the baseline hazard (Weibull, piecewise constant and exponential of a B-spline), with the possibility to include time-dependent and/or non-linear effect(s) of variable(s) and a random effect defined at the cluster level. The time-dependent effect of a covariate is modelled by adding interaction terms between the covariate and a function of time of the same class as the one used for the baseline hazard (in particular, with the same knots for piecewise constant hazards; and with the same degree and the same knots for B-spline functions). The random effect is assumed to be normally distributed with mean 0 and standard deviation sigma. The optimisation process uses adaptive Gaussian quadrature to calculate the cluster-specific marginal likelihoods. The logarithm of the full marginal likelihood, defined as the sum of the logarithms of the cluster-specific marginal likelihoods, is then maximised using optimisation routine such as nlm or optim.
● Data Source:
CranContrib
● Keywords:
● Alias: mexhaz
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summary.mexhaz
(Package: mexhaz) :
Summary method for a mexhaz object
Display the model call, the values of the estimated model parameters, as well as the corresponding hazard ratios (only for proportional effects).
● Data Source:
CranContrib
● Keywords:
● Alias: summary.mexhaz
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print.predMexhaz
(Package: mexhaz) :
Print method for a predMexhaz object
Display the first lines of the data.frame containing the predictions provided by the predMexhaz function.
● Data Source:
CranContrib
● Keywords:
● Alias: print.predMexhaz
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simdatn1
(Package: mexhaz) :
Simulated dataset
The simdatn1 dataset has 4000 rows and 8 columns.
● Data Source:
CranContrib
● Keywords:
● Alias: simdatn1
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predMexhaz
(Package: mexhaz) :
Predictions based on a mexhaz model
Function for predicting the (excess) hazard and the corresponding (net) survival from a model fitted with the mexhaz function for a particular vector of covariates. If the survival model was fitted with an expected hazard, the estimates obtained are excess hazard and net survival estimates. When the model includes a random effect, the predicted values are obtained for the value 0 of the random effect. Confidence limits can be obtained by Monte-Carlo simulation (for all types of baseline hazard) and by the Delta Method (not available for Weibull hazard). This function allows the computation of the hazard and the survival at one time point for several vectors of covariables or for one vector of covariables at several time points.
● Data Source:
CranContrib
● Keywords:
● Alias: predMexhaz
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points.predMexhaz
(Package: mexhaz) :
Points method for a predMexhaz object
Function for adding to an already existing graphical window the predicted (excess) hazard or (net) survival based on a predMexhaz object.
● Data Source:
CranContrib
● Keywords:
● Alias: points.predMexhaz
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print.mexhaz
(Package: mexhaz) :
Print method for a mexhaz object
Display the model call as well as the values of the estimated model parameters.
● Data Source:
CranContrib
● Keywords:
● Alias: print.mexhaz
●
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mexhaz-package
(Package: mexhaz) :
Mixed effect parametric excess hazard models
Fit an (excess) hazard regression model using different shapes for the baseline hazard (Weibull, piecewise constant and exponential of a B-spline), with the possibility to include time-dependent and/or non-linear effect(s) of variable(s) and a random effect defined at the cluster level. The time-dependent effect of a covariate is modelled by adding interaction terms between the covariate and a function of time of the same class as the one used for the baseline hazard (in particular, with the same knots for piecewise constant hazards; and with the same degree and the same knots for B-spline functions). The random effect is assumed to be normally distributed with mean 0 and standard deviation sigma. The optimisation process uses adaptive Gaussian quadrature to calculate the cluster-specific marginal likelihoods. The logarithm of the full marginal likelihood, defined as the sum of the logarithms of the cluster-specific marginal likelihoods, is then maximised using optimisation routines such as nlm or optim. Functions to compute and plot the predicted (excess) hazard and (net) survival are provided.
● Data Source:
CranContrib
● Keywords:
● Alias: mexhaz-package
●
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