PROJECT SUMMARY/ABSTRACT
In 2015, the World Health Organization (WHO) introduced Treat-All guidelines for people living with HIV, which
recommend immediate initiation of antiretroviral therapy (ART) treatment upon diagnosis regardless of disease
severity. Since then, most countries worldwide have adopted the policy. However, the understanding of the impact of
such policy is quite limited, especially regarding HIV disease progression. Focused on event history outcome
(represented by WHO clinical stages and death), we recently conducted a preliminary analysis. We used data from
the Central Africa region of the International epidemiology Database to Evaluate AIDS (CA-IeDEA) for a multistate
model based on a target trial design (where two cohorts were constructed, one before and one after the policy
adoption). This work illuminated several limitations. For example, the assumption of non-informative censoring was
unlikely to hold for all censored individuals due to loss of follow-up or transfer out. Also, the relatively small sample
size of the CA-IeDEA hindered our capacities to 1) explore more clinically relevant and biologically plausible models
for HIV disease progression and 2) explore population heterogeneities regarding the impact of the Treat-All on the
outcome. In the proposed study, we plan to address these limitations by developing new statistical methods and
leveraging the multi-regional, i.e., the global-IeDEA data, which will provide a substantially larger sample. We will
develop procedures to address informative (dependent) censoring for the multistate models under the target trial
design to allow for sensitivity analysis. For example, we propose parametric, nonparametric, and semi-parametric
approaches to handle censoring at random. In addition, we offer a controlled multiple imputation method to handle
censoring not at random. We will compare and validate those methods using both internal and external data. Finally,
we will comprehensively analyze the global-IeDEA data, where the sensitivity analysis will ensure the robustness of
our findings. The proposed work will advance research in HIV care by providing more detailed information on possible
evolutionary courses of HIV disease progression and factors that modify the effectiveness of Treat-All. Our analysis
is a first step towards developing more precise patient treatment options and resource allocation, thereby improving
patient outcomes. The proposed statistical methods may also have applications to model other diseases that evolve
through predefined clinical states with intermittent data collection schema subject to similar data complexities.
