Abstract
BackgroundOrdinal 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.MethodsThe data is from an ongoing prospective cohort study conducted amongst adult women who are HIV-infected 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.ResultsMultilevel 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.ConclusionIt 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.
Highlights
Ordinal health longitudinal response variables have distributions that make them unsuitable for many popular statistical models that assume normality
Human Immunodeficiency Virus (HIV) infection causes a weakening of the immune system leading to the development of Human Immunodeficiency Syndrome (AIDS) in the vast majority of infected persons if left untreated
One of the key biomarkers which is a predictor of progression to Acquired Immune Deficiency Syndrome (AIDS), as well as a means of monitoring a combination of antiretroviral therapy is the CD4 cell count
Summary
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. One of the key biomarkers which is a predictor of progression to AIDS, as well as a means of monitoring a combination of antiretroviral therapy (cART) is the CD4 cell count. Low CD4 counts are associated with a greater risk of patients developing opportunistic infections, which may progress to advanced disease stage [1, 2]. We use a multilevel longitudinal ordinal model to examine factors associated with adverse events (initial status and rate of change) of HIV infected patients
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