Biometrics, 2017-03, Vol.73 (1), p.294-304
2017
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- Autor(en) / Beteiligte
- Titel
- Dynamic Models for Estimating the Effect of HAART on CD4 in Observational Studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study
- Ist Teil von
- Biometrics, 2017-03, Vol.73 (1), p.294-304
- Ort / Verlag
- United States: Wiley-Blackwell
- Erscheinungsjahr
- 2017
- Quelle
- Wiley Blackwell Single Titles
- Beschreibungen/Notizen
- 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.
- Sprache
- Englisch; Französisch
- Identifikatoren
- ISSN: 0006-341XeISSN: 1541-0420DOI: 10.1111/biom.12564Titel-ID: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5269533
- Format
- –
- Schlagworte
- Anti-HIV Agents - pharmacology, Antiretroviral drugs, Antiretroviral Therapy, Highly Active, Biological effects, Biological models (mathematics), BIOMETRIC PRACTICE, Causality, CD4 antigen, CD4 Lymphocyte Count, Clinical trials, Cohort analysis, Cohort Studies, Computer Simulation, Continuous time systems, Difference equations, Differential equations, Dynamic mechanistic models, Dynamic models, Dynamical Systems, Highly active antiretroviral therapy, HIV, Human immunodeficiency virus, Humans, Immunology, Immunotherapy, Lentivirus, Life Sciences, Linear increment models (LIM), Linear Models, Marginal structural models (MSM), Mathematical models, Mathematics, Medical research, Nonlinear equations, Non‐linear mixed effect models (NLME), Observational studies, Observational Studies as Topic, Observational study, Ordinary differential equation (ODE), Ordinary differential equations, Randomization, Retroviridae, Santé publique et épidémiologie, Statistical analysis, Statistical inference, Statistics, Treatment Outcome, Vaccinology, Viral Load