A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption

Abstract

Characterization of HIV viral rebound after the discontinuation of antiretroviral therapy is central to HIV cure research. We propose a parametric nonlinear mixed effects model for the viral rebound trajectory, which often has a rapid rise to a peak value followed by a decrease to a viral load set point. We choose a flexible functional form that captures the shapes of viral rebound trajectories and can also provide biological insights regarding the rebound process. Each parameter can incorporate a random effect to allow for variation in parameters across individuals. Key features of viral rebound trajectories such as viral set points are represented by the parameters in the model, which facilitates assessment of intervention effects and identification of important pretreatment interruption predictors for these features. We employ a stochastic expectation-maximization (StEM) algorithm to incorporate HIV-1 RNA values that are below the lower limit of assay quantification. We evaluate the performance of our model in simulation studies and apply the proposed model to longitudinal HIV-1 viral load data from five AIDS Clinical Trials Group treatment interruption studies. K E Y W O R D Scensoring, imputation, iterative, nonlinear, viral rebound

Inferring random change point from left-censored longitudinal data by segmented mechanistic nonlinear models, with application in HIV surveillance study

“…Determining convergence appears to be an open question for StEM. In the literature, the commonly used approach for convergence diagnostics is through visual examination of the trace plots (see, e.g., Yang, 2018, Wang et al, 2020, Huang et al, 2020. Recently, Zhang et al (2020) proposed a Geweke Statistics based method.…”

“…The StEM algorithm is more computationally efficient than the MCEM algorithm as only one realization of the missing data is required for each iteration (IP, 2002). Most recently, Wang et al (2020) extended the StEM algorithm to estimate parameters in VL dynamics models accounting for left-censored data; the authors showed that the resulting estimator is less biased than naive methods that either omit all censored data points or impute the censored observation with half of the quantification limit.…”

Analytical Treatment Interruption in HIV Trials: Statistical and Study Design Considerations

“…In general, clinical trial outcome measures and analysis methods are kept simple as discussed above but more complicated statistical modeling of viral rebound kinetics can also be applied [39,40]. However, these methods were developed using older ATI studies; substantive modeling complications might arise if applied to ATI trials with relatively short durations off ART such as studies with ART restart directly after viral rebound.…”

Stochastic Version of EM Algorithm for Nonlinear Random ChangePoint Models

“…For the convergence of a StEM algorithm, in literature, the commonly used approach is through visually examination of the trace plots (see, e.g., [18], [19], [20]). Recently, Zhang et al [9] proposed a Geweke Statistics based method.…”