# Likelihood Ratio Statistic to Compare Embedded Multistate Models This function computes a Likelihood Ratio Statistic to compare two embedded multistate models. ```r lrs.multistate(model1, model0) ``` ## Arguments - `model1`: A list containing the results after using a function included in the present `RISCA` package. - `model0`: A list containing the results after using a function included in the present `RISCA` package. The function used to obtained the `model0` have to be the same than the one used to obtain the `model1`. The `model0` have to be embedded in the `model1`. ## Returns - **statistic**: The value of the statistic. - **ddl**: The degrees of freedom. - **pvalue**: The p-value. ## Author(s) Yohann Foucher Florence Gillaizeau ## Examples ```r data(dataDIVAT1) # A subgroup analysis to reduce the time needed for this example dataDIVAT1$id<-c(1:nrow(dataDIVAT1)) set.seed(2) d3<-dataDIVAT1[dataDIVAT1$id %in% sample(dataDIVAT1$id, 250, replace = FALSE),] # To illustrate the use of a 3-state model, individuals with trajectory 13 and 123 are # censored at the time of transition into state X=3 d3$trajectory[d3$trajectory==13]<-1 d3$trajectory[d3$trajectory==123]<-12 d3$trajectory[d3$trajectory==14]<-13 d3$trajectory[d3$trajectory==124]<-123 # 3-state parametric semi-Markov model : does 'z' influence both the # transition 1->3 ? We only reduced the precision and the number of iteration # to save time in this example, prefere the default values. m1 <- semi.markov.3states(times1=d3$time1, times2=d3$time2, sequences=d3$trajectory, dist=c("E","E","E"), ini.dist.12=c(9.93), ini.dist.13=c(11.54), ini.dist.23=c(10.21), cov.12=d3$z, init.cov.12=c(-0.13), names.12=c("beta12_z"), cov.13=d3$z, init.cov.13=c(1.61), names.13=c("beta13_z"), conf.int=TRUE, silent=FALSE, precision=0.001) m1 m0 <- semi.markov.3states(times1=d3$time1, times2=d3$time2, sequences=d3$trajectory, dist=c("E","E","E"), ini.dist.12=c(9.93), ini.dist.13=c(11.54), ini.dist.23=c(10.21), cov.12=d3$z, init.cov.12=c(-0.13), names.12=c("beta12_z"), conf.int=TRUE, silent=FALSE, precision=0.001) m0 lrs.multistate(model1=m1, model0=m0) ```

lrs.multistate function