Dynamical models of biomarkers and clinical progression for personalized medicine: The HIV context

Prague, Mélanie; Commenges, Daniel; Thiébaut, Rodolphe

doi:10.1016/j.addr.2013.04.004

Cited by 15 publications

(11 citation statements)

References 132 publications

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“…(Perelson, ) give a review of some models for HIV dynamics based on ODE systems. In this article, we consider the “target cells model”, called Model 7 and described bellow, which proved to provide a good fit and prediction abilities (Prague et al, ).…”

Section: Modeling the Treatment Effect In Observational Studiesmentioning

confidence: 99%

Dynamic Models for Estimating the Effect of HAART on CD4 in Observational Studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study

et al. 2016

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Summary Highly active antiretroviral therapy (HAART) has proved efficient in increasing CD4 counts in many randomized clinical trials. Because randomized trials have some limitations (e.g., short duration, highly selected subjects), it is interesting to assess the effect of treatments using observational studies. This is challenging because treatment is started preferentially in subjects with severe conditions. This general problem had been treated using Marginal Structural Models (MSM) relying on the counterfactual formulation. Another approach to causality is based on dynamical models. We present three discrete-time dynamic models based on linear increments models (LIM): the first one based on one difference equation for CD4 counts, the second with an equilibrium point, and the third based on a system of two difference equations, which allows jointly modeling CD4 counts and viral load. We also consider continuous-time models based on ordinary differential equations with non-linear mixed effects (ODE-NLME). These mechanistic models allow incorporating biological knowledge when available, which leads to increased statistical evidence for detecting treatment effect. Because inference in ODE-NLME is numerically challenging and requires specific methods and softwares, LIM are a valuable intermediary option in terms of consistency, precision and complexity. We compare the different approaches in simulation and in illustration on the ANRS CO3 Aquitaine Cohort and the Swiss HIV Cohort Study.

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Section: Modeling the Treatment Effect In Observational Studiesmentioning

confidence: 99%

Dynamic Models for Estimating the Effect of HAART on CD4 in Observational Studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study

et al. 2016

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“…Our approach could equally be used with other methods. Models that describe the progression of a specific disease by using differential equations are another option, such as the HIV model in Prague et al (2013). Different models, including a version of Farewell's linear increments model, have also been compared in other studies (Prague et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Estimating the Treatment Effect on the Treated Under Time-Dependent Confounding in an Application to the Swiss HIV Cohort Study

Gran

Hoff

Røysland

et al. 2017

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary. When comparing time varying treatments in a non-randomized setting, one must often correct for time-dependent confounders that influence treatment choice over time and that are themselves influenced by treatment. We present a new two-step procedure, based on additive hazard regression and linear increments models, for handling such confounding when estimating average treatment effects on the treated. The approach can also be used for mediation analysis. The method is applied to data from the Swiss HIV Cohort Study, estimating the effect of antiretroviral treatment on time to acquired immune deficiency syndrome or death. Compared with other methods for estimating the average treatment effects on the treated the method proposed is easy to implement by using available software packages in R.

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“…Differential equations and other dynamic models are also present in medicine, see Prague et al () & Prague et al () for applications to human immunodeficiency virus (HIV) infection. A more complex example is the so‐called dynamic causal model, which has been developed in neurophysiology for trying to understand mechanisms in the brain and neural system (Stephan et al, ).…”

Section: Causality and Mechanism In A Process Settingmentioning

confidence: 99%

Feedback and Mediation in Causal Inference Illustrated by Stochastic Process Models

Aalen

Gran

Røysland

et al. 2017

Scandinavian J Statistics

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The concept of causality is naturally related to processes developing over time. Central ideas of causal inference like time-dependent confounding (feedback) and mediation should be viewed as dynamic concepts. We shall study these concepts in the context of simple dynamic systems. Time-dependent confounding and its implications are illustrated in a Markov model. We emphasize the distinction between average treatment effect, ATE, and treatment effect of the treated, ATT. These effects could be quite different, and we discuss the relationship between them. Mediation is studied in a stochastic differential equation model. A type of natural direct and indirect effects is considered for this model. Mediation analysis of discrete measurements from such processes may give misleading results, and one needs to consider the underlying continuous process. The dynamic and time-continuous view of causality and mediation is an essential feature, and more attention should be payed to the time aspect in causal inference.

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Dynamical models of biomarkers and clinical progression for personalized medicine: The HIV context

Cited by 15 publications

References 132 publications

Dynamic Models for Estimating the Effect of HAART on CD4 in Observational Studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study

Dynamic Models for Estimating the Effect of HAART on CD4 in Observational Studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study

Estimating the Treatment Effect on the Treated Under Time-Dependent Confounding in an Application to the Swiss HIV Cohort Study

Feedback and Mediation in Causal Inference Illustrated by Stochastic Process Models

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