Preprints of ongoing projects

Leveraging classical analysis of cluster randomized trials with contact network information in infectious diseases.

P. Staples, M. Prague, JP. Onnela and V. De gruttola

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Abstract

We develop methods to leverage information on contact networks to improve efficiency and validity of analysis of data from cluster-randomized trials (CRTs) designed to investigate the impact of prevention interventions. These methods make use of flexible approaches for network generation that are based on degree-corrected stochastic block models and that are potentially applicable for a wide range of transmissible diseases. A semi-parametric doubly robust estimator is useful for estimating the marginal effect of the intervention while adjusting for imbalance in covariates and missing information. Simulations of CRTs show that adjusting for network features increases efficiency, in some cases, by more than 30% and leads to considerable increase of the statistical power while retaining coverage near the nominal value of 95%. We identify the major network features driving these improvements, such as the number of first- and second-degree contacts, the number of closed sub-communities, and the shortest path to an infected individual. Because network information is difficult to collect and networks are likely to be modeled with considerable uncertainty, we perform a sensitivity analysis evaluating the degree to which it is profitable to use partial or approximate information. Adjustments for covariates measured with error based on regression calibration are investigated.

Bibtex

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Key words :

Cluster randomized trial; Epidemics; GEE; Networks; Stochastic-block model

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GEE Analysis of Cluster Randomized Controlled Trials with Missing Data: An orientation.

L. Turner and M. Prague

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Abstract

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Key words :

Cluster randomized trial; Education; GEE; IPW; Missing data

Analytics of the Giant Component and SIR Infectious Sizes For Stochastic Blockmodel with degree correction networks.

P. Staples, M. Prague, V. De gruttola and JP. Onnela

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Abstract

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Key words :

Epidemics; Giant component; Networks; Stochastic-block model; SI; SIR

From in vitro to in vivo quantification of antiretroviral drugs effects based on dynamical models of HIV.

Prague Mélanie, Alison Hill and Daniel Rosenbloom

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Abstract

Population dynamics of HIV and CD4+ T cells can be modeled with Ordinary Differential Equations (ODE). We aim at quantifying the in vivo effect of combinations of antiretroviral drugs treatments (cARTs) by a function of the effects of the antiretroviral drugs (ARVs) in the combination. To estimate the ARVs effects we must have a large dataset and it is desirable to add external information to ensure identifiability. An adequate modeling of in vitro assays yields such information. Recent single-round infectivity assays allowed quantify- ing the dose-response curves in vitro: the instantaneous inhibitory potential (IIP) has been established as a measure of ARVs activity. The IIPs of cARTs can be viewed as a function of ARV’s IIPs based on known interactions. Bliss independence is a convenient assumption to build dynamical models. Random effects account for inter-individual variability of IIPs that may result from host and virus genetics. Finally, more flexibility is provided by estimating an in vitro to in vivo conversion factor. We used a Bayesian approach for estimating the ARVs effects: cARTs effects follow by computation. We demonstrate that this model has good fit abilities and that our approach opens the perspective to deeper the understanding of treatment action regarding adherence and latent reservoirs and to envisage treatment choice optimization. This analysis is applied to a dataset of 350 patients taking 7 different antiretroviral drugs (AZT, D4T, ddI, 3TC, LPV, APV, DRV) from two stand-alone clinical trial: ALBI, PUZZLE, and two clinical trials nested in the ANRS CO3 Aquitaine Cohort: ZEPHIR and PREDIZISTA.

Bibtex

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Key words :

Antiretroviral therapy; in vitro; in vivo; Treatment effect; mechanistic models; ODE; clinical trials; observational studies.

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