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Dynamic models for estimating the effect of HAART on CD4 in observational studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study.
Prague, Mélanie; Commenges, Daniel; Gran, Jon Michael; Ledergerber, Bruno; Young, Jim; Furrer, Hansjakob; Thiébaut, Rodolphe.
Afiliação
  • Prague M; Harvard T.H. Chan School of Public Health, Biostatistics Department, Boston, U.S.A.
  • Commenges D; University of Bordeaux, ISPED, F-33000 Bordeaux, France.
  • Gran JM; INSERM, U1219 Bordeaux Population Health Research Centre, F-33000, Bordeaux, France.
  • Ledergerber B; INRIA (SISTM) Centre Recherche Bordeaux Sud-Ouest, University of Bordeaux, Talence, France.
  • Young J; Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital and University of Oslo, Norway.
  • Furrer H; Division of Infectious Diseases and Hospital Epidemiology, University Hospital of Zurich, Switzerland.
  • Thiébaut R; Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital of Basel, Switzerland.
Biometrics ; 73(1): 294-304, 2017 03.
Article em En | MEDLINE | ID: mdl-27461460
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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Lineares / Causalidade / Contagem de Linfócito CD4 / Fármacos Anti-HIV / Terapia Antirretroviral de Alta Atividade Tipo de estudo: Clinical_trials / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Lineares / Causalidade / Contagem de Linfócito CD4 / Fármacos Anti-HIV / Terapia Antirretroviral de Alta Atividade Tipo de estudo: Clinical_trials / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article