From Descriptive to Mechanistic Models to Study Causal Effects: Application to the Effect of HAART on CD4 Count

• Activity Number: 230
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Melanie Prague*+ and Daniel Commenges and Jon Michael Gran and Odd Aalen and Rodolphe Thiébaut
• Companies: Harvard School of Public Health and University Bordeaux INSERM U897 ISPED and University of Oslo and University of Oslo and INSERM/INRIA
• Keywords: causality ; marginal structural models ; mechanistic models ; mixed effects ; ODE ; dynamical models

Abstract

The problem of assessing the effect of a treatment on a marker in observational studies raises the difficulty that attribution of the treatment may depend on the observed marker values. This problem has been treated using marginal structural models relying on the counterfactual formalism. Another approach to causality is based on dynamical models where causal influence has been formalized in the framework of stochastic processes with linear increments models. This can be further extended in continuous time with mechanistic models, particularly based on ordinary differential equations with random effects, and allows incorporating biological knowledge. We show that a continuum can be established between descriptive and mechanistic modeling. Although inference in mechanistic models is challenging and requests specific methods, these models can yield more powerful and reliable results with qualitative interpretation such as mediation. The different approaches are illustrated by estimating the effect of highly active antiretroviral treatment (HAART) on CD4 count in a simulation study, then, in an observational study of HIV infected subjects (ANRS CO3 Aquitaine Cohort).