anova: Anova Method for Fitted Joint Models

In drizopoulos/JM: Joint Modeling of Longitudinal and Survival Data

Anova Method for Fitted Joint Models

Description

Produces marginal Wald tests or Performs a likelihood ratio test between two nested joint models.

Usage

## S3 method for class 'jointModel'
anova(object, object2, test = TRUE, 
   process = c("both", "Longitudinal", "Event"), L = NULL, ...)

Arguments

object	an object inheriting from class jointModel, nested in object2.
object2	an object inheriting from class jointModel.
test	logical; if TRUE the likelihood ratio test is performed.
process	for which of the two submodels to produce the marginal Wald tests table.
L	a numeric matrix of appropriate dimensions defining the contrasts of interest.
...	additional arguments; currently none is used.

Value

An object of class aov.jointModel with components,

nam0	the name of object.
L0	the log-likelihood under the null hypothesis (object).
aic0	the AIC value for the model given by object.
bic0	the BIC value for the model given by object.
nam1	the name of object2.
L1	the log-likelihood under the alternative hypothesis (object2).
aic1	the AIC value for the model given by object2.
bic1	the BIC value for the model given by object2.
df	the degrees of freedom for the test (i.e., the difference in the number of parameters).
LRT	the value of the Likelihood Ratio Test statistic (returned if test = TRUE).
p.value	the p-value of the test (returned if test = TRUE).
aovTab.Y	a data.frame with the marginal Wald tests for the longitudinal process; produced only when object2 is missing.
aovTab.T	a data.frame with the marginal Wald tests for the event process; produced only when object2 is missing.
aovTab.L	a data.frame with the marginal Wald tests for the user-defined contrasts matrix; produced only when object2 is missing and L is not NULL.

Warning

The code minimally checks whether the models are nested! The user is responsible to supply nested models in order the LRT to be valid.

Author(s)

Dimitris Rizopoulos [e-mail]

References

Rizopoulos, D. (2012) Joint Models for Longitudinal and Time-to-Event Data: with Applications in R. Boca Raton: Chapman and Hall/CRC.

Rizopoulos, D. (2010) JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data. Journal of Statistical Software 35 (9), 1–33. doi: 10.18637/jss.v035.i09

See Also

jointModel

Examples

## Not run: 
# linear mixed model fit without treatment effect
fitLME.null <- lme(sqrt(CD4) ~ obstime, 
    random = ~ 1 | patient, data = aids)
# cox model fit without treatment effect
fitCOX.null <- coxph(Surv(Time, death) ~ 1, 
    data = aids.id, x = TRUE)
# joint model fit without treatment effect
fitJOINT.null <- jointModel(fitLME.null, fitCOX.null, 
    timeVar = "obstime", method = "weibull-PH-aGH")

# linear mixed model fit with treatment effect
fitLME.alt <- lme(sqrt(CD4) ~ obstime * drug - drug, 
    random = ~ 1 | patient, data = aids)
# cox model fit with treatment effect
fitCOX.alt <- coxph(Surv(Time, death) ~ drug, 
    data = aids.id, x = TRUE)
# joint model fit with treatment effect
fitJOINT.alt <- jointModel(fitLME.alt, fitCOX.alt, timeVar = "obstime", 
    method = "weibull-PH-aGH")

# likelihood ratio test for treatment effect
anova(fitJOINT.null, fitJOINT.alt)

## End(Not run)

drizopoulos/JM documentation built on Aug. 10, 2022, 1:51 a.m.