Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women

Abstract

Background: Ordinal health longitudinal response variables have distributions that make them unsuitable for many popular statistical models that assume normality. We present a multilevel growth model that may be more suitable for medical ordinal longitudinal outcomes than are statistical models that assume normality and continuous measurements. Methods: The data is from an ongoing prospective cohort study conducted amongst adult women who are HIVinfected patients in Kwazulu-Natal, South Africa. Participants were enrolled into the acute infection, then into early infection subsequently into established infection and afterward on cART. Generalized linear multilevel models were applied. Results: Multilevel ordinal non-proportional and proportional-odds growth models were presented and compared. We observed that the effects of covariates can't be assumed identical across the three cumulative logits. Our analyses also revealed that the rate of change of immune recovery of patients increased as the follow-up time increases. Patients with stable sexual partners, middle-aged, cART initiation, and higher educational levels were more likely to have better immunological stages with time. Similarly, patients having high electrolytes component scores, higher red blood cell indices scores, higher physical health scores, higher psychological well-being scores, a higher level of independence scores, and lower viral load more likely to have better immunological stages through the follow-up time. Conclusion: It can be concluded that the multilevel non-proportional-odds method provides a flexible modeling alternative when the proportional-odds assumption of equal effects of the predictor variables at every stage of the response variable is violated. Having higher clinical parameter scores, higher QoL scores, higher educational levels, and stable sexual partners were found to be the significant factors for trends of CD4 count recovery.

Individual and community-level determinants of childhood vaccination in Ethiopia

“…More importantly, it is often the cases of the respondents are observed nested within clusters or communities (i.e children nested with community/clusters) and so the use of the ordinal regression model which assumes the observations are independent which is problematic. In this case, the multilevel regression model better analyzes the response measurements [ 29 , 30 ]. A Multilevel Proportional Odds Model (MPOM) was used to identify the individual and community-level factors associated with child vaccination.…”

“…A Multilevel Proportional Odds Model (MPOM) was used to identify the individual and community-level factors associated with child vaccination. The proportionality assumptions for MPOM were checked by using Chi-square parallel line tests [ 29 ]. MPOM model contains both fixed and random effects.…”