Multi-state models for defining degrees of chronicity related to HIV-infected patient therapy adherence

Oliveira, Raquel; Shimakura, Sílvia Emiko; Campos, Dayse Pereira; Victoriano, Flaviana Pavan; Ribeiro, Sayonara Rocha; Veloso, Valdiléa G.; Grinsztejn, Beatriz; Carvalho, Marília Sá

doi:10.1590/s0102-311x2013000400017

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…In this study, full-parametric and semi-parametric multistate Markov frailty models were used to model viral rebound, viral suppression, and state-specific duration in HIV-infected patients under treatment. Multistate frailty models are a powerful tool for modeling complex cycles of chronic diseases, encompassing the life history of a cohort [29], considering all possible pathways [30], and further allowing for dealing with heterogeneity between the sequence of transitions [31,32]. These models can also accommodate competing risks, censored data, recurrent outcomes ,and multiple outcomes [33].…”

Section: Introductionmentioning

confidence: 99%

Modeling Viral Suppression, Viral Rebound and State-Specific Duration of HIV Patients with CD4 Count Adjustment: Parametric Multistate Frailty Model Approach

et al. 2020

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Introduction: Combination antiretroviral therapy has become the standard care of human immunodeficiency virus (HIV)-infected patients and has further led to a dramatically decreased progression probability to acquired immune deficiency syndrome (AIDS) for patients under such a therapy. However, responses of the patients to this therapy have recorded heterogeneous complexity and high dynamism. In this paper, we simultaneously model long-term viral suppression, viral rebound, and statespecific duration of HIV-infected patients. Methods: Full-parametric and semi-parametric Markov multistate models were applied to assess the effects of covariates namely TB co-infection, educational status, marital status, age, quality of life (QoL) scores, white and red blood cell parameters, and liver enzyme abnormality on long-term viral suppression, viral rebound and state-specific duration for HIV-infected individuals before and after treatment. Furthermore, two models, one including and another excluding the effect of the frailty, were presented and compared in this study. Results: Results from the diagnostic plots, Akaike information criterion (AIC) and likelihood ratio test showed that the Weibull multistate frailty model fitted significantly better than the exponential and semi-parametric multistate models. Viral rebound was found to be significantly associated with many sex partners, higher eosinophils count, younger age, lower educational level, higher monocyte counts, having abnormal neutrophils count, and higher liver enzyme abnormality. Furthermore, viral suppression was also found to be significantly associated with higher QoL scores, and having a stable sex partner. The analysis result also showed that patients with a stable sex partner, higher educational levels, higher QoL scores, lower eosinophils count, lower monocyte counts, and higher RBC indices were more likely to spend more time in undetectable viral load state. Conclusions: To achieve and maintain the UNAIDS 90% suppression targets, additional interventions are required to optimize antiretroviral therapy outcomes, specifically targeting those with poor clinical characteristics, lower education, younger age, and those with many sex partners. From a methodological perspective, the parametric multistate approach Digital Features To view digital features for this article go to

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Section: Introductionmentioning

confidence: 99%

Modeling Viral Suppression, Viral Rebound and State-Specific Duration of HIV Patients with CD4 Count Adjustment: Parametric Multistate Frailty Model Approach

et al. 2020

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“…Utilizing separate analyses for each single endpoint (the usual approach in many studies) 11 does not provide the possibility of uncovering relationships between different endpoints. 12,13,21 Using multi-state models improves the understanding of variation in risk factors related to the evolution of diseases considerably 22 and constructing them allows for a comprehensive view of a disease process. They can use to obtain the probabilities and hazards of occurrence of different events.…”

Section: Discussionmentioning

confidence: 99%

Identifying risk factors for progression to AIDS and mortality post-HIV infection using illness-death multistate model

Hamidi¹,

Tapak

Poorolajal

et al. 2017

Clinical Epidemiology and Global Health

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Background: There has remained a need to better understanding of prognostic factors that affect the survival or risk in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), particularly in developing countries. The aim of the present study aimed to identify the prognostic factors influencing AIDS progression in HIV positive patients in Hamadan province of Iran, using random survival forest in the presence of competing risks (death from causes not related to AIDS). This method considers all interactions between variables and their nonlinear effects. Methods: A data set of 585 HIV-infected patients extracted from 1997 to 2011 was utilized. The effect of several prognostic factors on cumulative incidence function (probability) of AIDS progression and death were investigated. Results: The used model indicated that using antiretroviral therapy tuberculosis co-infection are two top most important variables in predicting cumulative incidence function for AIDS progression in the presence of competing risks, respectively. The patients with tuberculosis had much higher predicted cumulative incidence probability. Predicted cumulative incidence probability of AIDS progression was also higher for mother to child mode of HIV transmission. Moreover, transmission type and gender were two top most important variables for the competing event. Men and those patients with IDUS transmission mode had higher predicted risk compared to others. Conclusions: Considering nonlinear effects and interaction between variables, confection with tuberculosis was the most important variable in prediction of cumulative incidence probability of AIDS progression.

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“…Most methodological developments have usually focused on semi-parametric methods, by using the Cox regression method as the basic framework of multi-state modelling [11,[13][14][15][16]. The fully parametric multi-state approach is less well known and somewhat of new development in medical data, where the same distribution is assumed for all transitions [2,17].…”

Section: Introductionmentioning

confidence: 99%

Modelling HIV disease process and progression in seroconversion among South Africa women: using transition-specific parametric multi-state model

Dessie

Zewotir

Mwambi

et al. 2020

Theor Biol Med Model

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Background: HIV infected patients may experience many intermediate events including between-event transition throughout their follow up. Through modelling these transitions, we can gain a deeper understanding of HIV disease process and progression and of factors that influence the disease process and progression pathway. In this work, we present transition-specific parametric multi-state models to describe HIV disease process and progression. Methods: The data is from an ongoing prospective cohort study conducted amongst adult women who were HIVinfected in KwaZulu-Natal, South Africa. Participants were enrolled during the acute HIV infection phase and then followed up during chronic infection, up to ART initiation. Results: Transition specific distributions for multi-state models, including a variety of accelerated failure time (AFT) models and proportional hazards (PH) models, were presented and compared in this study. The analysis revealed that women enrolling with a CD4 count less than 350 cells/mm 3 (severe and advanced disease stages) had a far lower chance of immune recovery, and a considerably higher chance of immune deterioration, compared to women enrolling with a CD4 count of 350 cells/mm 3 or more (normal and mild disease stages). Our analyses also showed that older age, higher educational levels, higher scores for red blood cell counts, higher mononuclear scores, higher granulocytes scores, and higher physical health scores, all had a significant effect on a shortened time to immunological recovery, while women with many sex partners, higher viral load and larger family size had a significant effect on accelerating time to immune deterioration. Conclusion: Multi-state modelling of transition-specific distributions offers a flexible tool for the study of demographic and clinical characteristics' effects on the entire disease progression pathway. It is hoped that the article will help applied researchers to familiarize themselves with the models, including interpretation of results.

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Multi-state models for defining degrees of chronicity related to HIV-infected patient therapy adherence

Cited by 3 publications

References 27 publications

Modeling Viral Suppression, Viral Rebound and State-Specific Duration of HIV Patients with CD4 Count Adjustment: Parametric Multistate Frailty Model Approach

Modeling Viral Suppression, Viral Rebound and State-Specific Duration of HIV Patients with CD4 Count Adjustment: Parametric Multistate Frailty Model Approach

Identifying risk factors for progression to AIDS and mortality post-HIV infection using illness-death multistate model

Modelling HIV disease process and progression in seroconversion among South Africa women: using transition-specific parametric multi-state model

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