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Table 2 Model selection process for the various model fitted for both viral load and CD4 cell counts multistate models

From: Markov modelling of viral load adjusting for CD4 orthogonal variable and multivariate conditional autoregressive mapping of the HIV immunological outcomes among ART patients in Zimbabwe

Covariate −2*LLa AICb Models compared LRT c statistic Dfd P-value
Viral load multistate model
Model 1-
No covariates
7074.605  
Model 2-
CD4 effects
7014.406 7062.99 Model 1 vs Model 2 59.61457 12 2.65e-08**
Model 3-
CD4 time-varying effects
7007.724 7055.72 Model 1 vs Model 3 66.88163 12 1.22e-09**
Model 4-
CD4 effects+ CD4 time-varying effects
6930.714 7002.71 Model 2 vs Model 4 83.69604 12 8.10e-13**
Model 3 vs Model 4 77.01372 12 1.53e-11**
CD4 cell counts multistate model
Model 1-
No covariates
7334.08  
Model 2-
VL effects
7298.644 7342.64 Model 1 vs Model 2 35.36379 11 2.16e-04**
Model 3-
VL time-varying effects
7256.166 7344.17 Model 1 vs Model 3 77.84109 33 1.74e-05**
Model 4-
VL effects + VLtime-varying effects
7227.461 7337.46 Model 2 vs Model 4 71.18325 33 1.28e-04**
Model 3 vs Model 4 28.70595 11 2.52e-03**
  1. aLog-likelihood bAkaike’s information criterion cLikelihood ratio test ddegrees of freedom **Significant at 5%