The stochastic system approach for estimating dynamic treatments effect
| LEADER | caa a22 4500 | ||
|---|---|---|---|
| 001 | 605476144 | ||
| 003 | CHVBK | ||
| 005 | 20210128100355.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20151001xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s10985-015-9322-3 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10985-015-9322-3 | ||
| 245 | 0 | 4 | |a The stochastic system approach for estimating dynamic treatments effect |h [Elektronische Daten] |c [Daniel Commenges, Anne Gégout-Petit] |
| 520 | 3 | |a 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. As an example, we focus on the analysis of the effect of a HAART on CD4 counts, where attribution of the treatment may depend on the observed marker values. This problem has been treated using marginal structural models relying on the counterfactual/potential response formalism. Another approach to causality is based on dynamical models, and causal influence has been formalized in the framework of the Doob-Meyer decomposition of stochastic processes. Causal inference however needs assumptions that we detail in this paper and we call this approach to causality the "stochastic system” approach. First we treat this problem in discrete time, then in continuous time. This approach allows incorporating biological knowledge naturally. When working in continuous time, the mechanistic approach involves distinguishing the model for the system and the model for the observations. Indeed, biological systems live in continuous time, and mechanisms can be expressed in the form of a system of differential equations, while observations are taken at discrete times. Inference in mechanistic models is challenging, particularly from a numerical point of view, but these models can yield much richer and reliable results. | |
| 540 | |a Springer Science+Business Media New York, 2015 | ||
| 690 | 7 | |a Causality |2 nationallicence | |
| 690 | 7 | |a Doob-Meyer decomposition |2 nationallicence | |
| 690 | 7 | |a HAART |2 nationallicence | |
| 690 | 7 | |a marginal structural models |2 nationallicence | |
| 690 | 7 | |a Dynamic treatment |2 nationallicence | |
| 690 | 7 | |a Mechanistic models |2 nationallicence | |
| 690 | 7 | |a Stochastic processes |2 nationallicence | |
| 690 | 7 | |a Stochastic systems |2 nationallicence | |
| 700 | 1 | |a Commenges |D Daniel |u INSERM U897, University of Bordeaux, Bordeaux, France |4 aut | |
| 700 | 1 | |a Gégout-Petit |D Anne |u Institut Elie Cartan, UMR CNRS 7502, Univ. de Lorraine, Nancy, France |4 aut | |
| 773 | 0 | |t Lifetime Data Analysis |d Springer US; http://www.springer-ny.com |g 21/4(2015-10-01), 561-578 |x 1380-7870 |q 21:4<561 |1 2015 |2 21 |o 10985 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10985-015-9322-3 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10985-015-9322-3 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Commenges |D Daniel |u INSERM U897, University of Bordeaux, Bordeaux, France |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Gégout-Petit |D Anne |u Institut Elie Cartan, UMR CNRS 7502, Univ. de Lorraine, Nancy, France |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Lifetime Data Analysis |d Springer US; http://www.springer-ny.com |g 21/4(2015-10-01), 561-578 |x 1380-7870 |q 21:4<561 |1 2015 |2 21 |o 10985 | ||