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. The data is from an ongoing prospective cohort study conducted amongst adult women who were HIV-infected 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. 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/mm3 (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/mm3 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. 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.

中文翻译：

---

对南非妇女中艾滋病毒疾病过程和血清转化进展进行建模：使用特定于过渡的参数多状态模型。

HIV 感染患者可能会经历许多中间事件，包括整个随访过程中的事件间过渡。通过对这些转变进行建模，我们可以更深入地了解艾滋病毒疾病过程和进展以及影响疾病过程和进展途径的因素。在这项工作中，我们提出了特定于转变的参数多状态模型来描述艾滋病毒疾病的过程和进展。这些数据来自一项正在进行的前瞻性队列研究，对象是南非夸祖鲁-纳塔尔省感染艾滋病毒的成年女性。参与者在急性艾滋病毒感染阶段入组，然后在慢性感染阶段进行随访，直至开始抗逆转录病毒治疗。本研究提出并比较了多状态模型的转换特定分布，包括各种加速失效时间 (AFT) 模型和比例风险 (PH) 模型。分析显示，与 CD4 计数低于 350 个细胞/mm3（严重和晚期疾病阶段）的女性相比，CD4 计数低于 350 个细胞/mm3 的女性免疫恢复的机会要低得多，免疫恶化的可能性要高得多。 350 个细胞/mm3 或更多（正常和轻度疾病阶段）。我们的分析还表明，年龄较大、教育水平较高、红细胞计数得分较高、单核得分较高、粒细胞得分较高以及身体健康得分较高，所有这些都会对缩短免疫恢复时间产生显着影响，而患有许多性伴侣、较高的病毒载量和较大的家庭规模对加速免疫恶化有显着影响。特定转变分布的多状态建模为研究人口和临床特征对整个疾病进展途径的影响提供了灵活的工具。希望本文能够帮助应用研究人员熟悉模型，包括结果的解释。