I have clinical data which lacks a golden standard for diagnosis. Instead multiple indices are used with different accuracy. I have transformed the results from these indices to binary for patients having and not having the disease.
This is then used for latent classification analysis. I want to know if I should use all samples for this analysis? or should I divide them and then use remaining samples for validation? Also, will I need to check for verification bias and how do I select a method to do that? How to check the category of each patient for diagnosis after that?
below is tabular form that I used to subject under latent classification analysis (LCA) using poLCA package in R
| Patient_ID | Index 1 | Index2 | Index3 |
|---|---|---|---|
| 1 | 1 | 1 | 2 |
| 2 | 2 | 2 | 1 |
the results I get are
Fit for 2 latent classes: =========================================================
number of observations: 65
number of estimated parameters: 7
residual degrees of freedom: 0
maximum log-likelihood: -116.6411 \AIC(2): 247.2821
BIC(2): 262.5028
G^2(2): 0.3041395 (Likelihood ratio/deviance statistic)
X^2(2): 0.291966 (Chi-square goodness of fit)
what type of graph will make me understand the results better? Thank you, your help will be appreciated here