I am approaching to Latent Class Analysis to identify "class" of patients based on some variables.

I am wondering whether I can use the classes allocation according to modal posterior probability as a covariate into a Cox-regression model, to analyse hazards across different class? To me, this sound like a suitable approach, but I would like a confirmation that this approach is ok, as I can't find much on this site.

Let's say (with R):

library(poLCA)
library(survival)
data <- whatever # Whatever dataset
    
form <- as.formula(cbind(X1, X2, X3 ...) ~ 1) 
  # Where X1, X2, X3 etc. are the variables used to run the latent 
  # class analysis
    
lc <- poLCA(form, data=data, nclass=3, nrep=5, maxiter=3000) 
  # Let's assume I have already evaluated BIC, entropy etc. and 
  #  found 3 as the best number of classes
    
data$class <- lc$predclass 
  # I use the class membership assigned by modal posterior probability
    
coxph(Surv(time, event) ~ class + Y1 + Y2 + Y3 ..., data=data) 
 # Where Y1, Y2, Y3 are other covariates I want to use in the 
 # Cox regression.

Would this approach be sound?

I am approaching to Latent Class Analysis to identify "classes" of patients based on some variables.

Question 1: diagnostics of the results. I already gone through the discussion on whether it is better to use BIC, cAIC, etc.: I reach the conclusion that evaluating several metrics, along with entropy and clinical judgment, probably is the best approach to choose the number of classes.

Now, several sources report that a "good entropy" is when entropy >0.8. But I have also found some other people saying that even a lower entropy (around 0.5) could be acceptable, if the classes makes sense, given that in certain areas reaching such high values of entropy (i.e., >0.8) may not be feasible. As the entropy for my latent class model is around 0.45, I want to ask whether "relaxing" the demand for high entropy is acceptable or not.

Question 2: I am wondering whether I can use the classes allocation according to modal posterior probability as a covariate into a Cox-regression model, to analyse hazards across different class? To me, this sound like a suitable approach, but I would like a confirmation that this approach is ok, as I can't find much on this site.

Let's say (with R):

library(poLCA)
library(survival)
data <- whatever # Whatever dataset
    
form <- as.formula(cbind(X1, X2, X3 ...) ~ 1) 
  # Where X1, X2, X3 etc. are the variables used to run the latent 
  # class analysis
    
lc <- poLCA(form, data=data, nclass=3, nrep=5, maxiter=3000) 
  # Let's assume I have already evaluated BIC, entropy etc. and 
  #  found 3 as the best number of classes
    
data$class <- lc$predclass 
  # I use the class membership assigned by modal posterior probability
    
coxph(Surv(time, event) ~ class + Y1 + Y2 + Y3 ..., data=data) 
 # Where Y1, Y2, Y3 are other covariates I want to use in the 
 # Cox regression model.

Would this approach be sound?

I am approaching to Latent Class Analysis to identify "class" of patients based on some variables.

I am wondering whether I can use the classes allocation according to modal posterior probability as a covariate into a Cox-regression model, to analyse hazards across different class? To me, this sound like a suitable approach, but I would like a confirmation that this approach is ok, as I can't find much on this site.

Let's say (with R):

library(poLCA)
library(survival)
data <- whatever #Whatever dataset

form <- as.formula(cbind(X1, X2, X3 ...) ~ 1) #Where X1, X2, X3 etc. are the variables used to run the latent class analysis

lc <- poLCA(form, data=data, nclass=3, nrep=5, maxiter=3000) #Let's assume I have already evaluated BIC, entropy etc. and found 3 as the best number of classes

data$class <- lc$predclass #I use the class membership assigned by modal posterior probability

coxph(Surv(time, event)~class+Y1+Y2+Y3..., data=data) #Where Y1, Y2, Y3 are other covariates I want to use in the Cox regression.

Would this approach be sound?

I am approaching to Latent Class Analysis to identify "class" of patients based on some variables.

I am wondering whether I can use the classes allocation according to modal posterior probability as a covariate into a Cox-regression model, to analyse hazards across different class? To me, this sound like a suitable approach, but I would like a confirmation that this approach is ok, as I can't find much on this site.

Let's say (with R):

library(poLCA)
library(survival)
data <- whatever # Whatever dataset
    
form <- as.formula(cbind(X1, X2, X3 ...) ~ 1) 
  # Where X1, X2, X3 etc. are the variables used to run the latent 
  # class analysis
    
lc <- poLCA(form, data=data, nclass=3, nrep=5, maxiter=3000) 
  # Let's assume I have already evaluated BIC, entropy etc. and 
  #  found 3 as the best number of classes
    
data$class <- lc$predclass 
  # I use the class membership assigned by modal posterior probability
    
coxph(Surv(time, event) ~ class + Y1 + Y2 + Y3 ..., data=data) 
 # Where Y1, Y2, Y3 are other covariates I want to use in the 
 # Cox regression.

Would this approach be sound?