title: Dynamic models for estimating the effect of HAART on CD4 in observational studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study.
abstract: 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.
authors.affiliation: Harvard T.H. Chan School of Public Health, Biostatistics Department, Boston, U.S.A.
authors.forename: Mélanie
authors.initials: M
authors.lastname: Prague
authors.affiliation: University of Bordeaux, ISPED, F-33000 Bordeaux, France.|INSERM, U1219 Bordeaux Population Health Research Centre, F-33000, Bordeaux, France.|INRIA (SISTM) Centre Recherche Bordeaux Sud-Ouest, University of Bordeaux, Talence, France.
authors.forename: Daniel
authors.initials: D
authors.lastname: Commenges
authors.affiliation: Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital and University of Oslo, Norway.
authors.forename: Jon Michael
authors.initials: JM
authors.lastname: Gran
authors.affiliation: Division of Infectious Diseases and Hospital Epidemiology, University Hospital of Zurich, Switzerland.
authors.forename: Bruno
authors.initials: B
authors.lastname: Ledergerber
authors.affiliation: Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital of Basel, Switzerland.
authors.forename: Jim
authors.initials: J
authors.lastname: Young
authors.affiliation: Department of Infectious Diseases Bern University Hospital, University of Bern, Switzerland.
authors.forename: Hansjakob
authors.initials: H
authors.lastname: Furrer
authors.affiliation: University of Bordeaux, ISPED, F-33000 Bordeaux, France.|INSERM, U1219 Bordeaux Population Health Research Centre, F-33000, Bordeaux, France.|INRIA (SISTM) Centre Recherche Bordeaux Sud-Ouest, University of Bordeaux, Talence, France.
authors.forename: Rodolphe
authors.initials: R
authors.lastname: Thiébaut
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology*; D023241:Antiretroviral Therapy, Highly Active; D018791:CD4 Lymphocyte Count; D015984:Causality; D015331:Cohort Studies; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D064887:Observational Studies as Topic; D016896:Treatment Outcome; D019562:Viral Load
chemical_list: D019380:Anti-HIV Agents
keywords: Dynamic mechanistic models; Linear increment models (LIM); Marginal structural models (MSM); Non-linear mixed effect models (NLME); Observational study; Ordinary differential equation (ODE)