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Journal ReviewReview

Modeling CD4+ T cells dynamics in HIV-infected patients receiving repeated cycles of exogenous Interleukin 7

Combination Antiretroviral Therapy (cART) succeeds to control viral replication in most HIV infected patients. This is normally followed by a reconstitution of the CD4$^+$ T cells pool; however, this does not happen for a substantial proportion of patients. For these patients, an immunotherapy based on injections of Interleukin 7 (IL-7) has been recently proposed as a co-adjutant treatment in the hope of obtaining long-term reconstitution of the T cells pool. Several questions arise as to the long-term efficiency of this treatment and the best protocol to apply. We develop a model based on a system of ordinary differential equations and a statistical model of variability and measurement. We can estimate key parameters of this model using the data from INSPIRE, INSPIRE 2 $\&$ INSPIRE 3 trials. In all three studies, cycles of three injections have been administered; in the last two studies, for the first time, repeated cycles of exogenous IL-7 have been administered. Our aim was to estimate the possible different effects of successive injections in a cycle, to estimate the effect of repeated cycles and to assess different protocols. The use of dynamical models together with our complex statistical approach allow us to analyze major biological questions. We found a strong effect of IL-7 injections on the proliferation rate; however, the effect of the third injection of the cycle appears to be much weaker than the first ones. Also, despite a slightly weaker effect of repeated cycles with respect to the initial one, our simulations show the ability of this treatment of maintaining adequate CD4$^+$ T cells count for years. We were also able to compare different protocols, showing that cycles of two injections should be sufficient in most cases. %Finally, we also explore the possibility of adaptive protocols

Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes

In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect affected and unaffected individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as efficiency covariates in exposure effect estimation. Using augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to simulated randomized trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias, power, and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. We also demonstrate the use of network-augmented GEEs on a clustered randomized trial evaluating the effects of wastewater monitoring on COVID-19 cases in residential buildings at the the University of California San Diego.Comment: Substantial revisio

CRTgeeDR: An R Package for Doubly Robust Generalized Estimating Equations Estimations in Cluster Randomized Trials with Missing Data

International audienceSemi-parametric approaches based on generalized estimating equation (GEE) are widelyused to analyse correlated outcomes. Most available softwares had been developed forlongitudinal settings. In this paper, we present a R package CRTgeeDR for estimatingparameters in marginal regression in cluster randomized trials (CRTs). Theory for adjustingfor missing at random outcomes by inverse-probability weighting methods (IPW)based on the use of a propensity score had been largely studied and implemented. Weexhibit that in CRTs most of the available softwares use an implementation of weightsthat lead to a bias in estimation if a non-independence working correlation structure ischosen. In CRTgeeDR, we solve this problem by using a different implementation whilekeeping the consistency properties of the IPW. Moreover, in CRTs using an augmentedGEE (AUG) allow to improve efficiency by adjusting for treatment-covariate interactionsand imbalance in baseline covariates between treatment groups using an outcome model.In CRTgeeDR, we extend the abilities of existing packages such as geepack and geeMto allow such data augmentation. Finally, one may want to combine IPW and AUG ina Doubly Robust (DR) estimator, which lead to consistent estimation when either thepropensity score or the outcome model corresponds to the true data generation process(Prague, Wang, Stephens, Tchetgen Tchetgen, and De gruttola 2015). The DR approachis implemented in CRTgeeDR. Simulations studies demonstrate the consistency of IPWimplemented in CRTgeeDR and the gains associated with the use of the DR for analyzinga binary outcome using a logit regression. Finally, we reanalyzed data from a sanitationCRT in developing countries (Guiteras, Levinsohn, and Mobarak 2015a) with the DRapproach compared to classical GEE and demonstrated a signiffcant intervention effect

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Use of dynamical models for treatment optimization in HIV infected patients : a sequential Bayesian analysis approach.

International audienceThe use of dynamic mechanistic models based on ordinary differential equations (ODE) has greatly improved the knowledge of the dynamics of HIV and of the immune system. Their flexibility for fitting data and prediction abilities make them a good tool for optimization of the design delivery and efficacy of new intervention in the HIV field. We present the problem of inference in ODE models with mixed effects on parameters. We introduce a Bayesian estimation procedure based on the maximization of the penalized likelihood and a normal approximation of posteriors, which is implemented in the NIMROD software. We investigate the impact of pooling different data by using a sequential Bayesian analysis (SBA), which uses posteriors of a previous study as new priors. We show that the normal approximation of the posteriors, which constrains the shape of new priors, leads to gains in accuracy of estimation while reducing computation times. The illustration is from two clinical trials of combination of antiretroviral therapies (cART): ALBI ANRS 070 and PUZZLE ANRS 104. This paper reproduces some unpublished work from my PhD thesis. It is an extension of my oral presentation on the same topic at the 47th Journées de Statistique organized by the French Statistical Society (SFdS) in Lille, France, May 2015, when being awarded the Marie-Jeanne Laurent-Duhamel prize