PMID- 27461460
OWN - NLM
STAT- MEDLINE
DCOM- 20170927
LR  - 20181113
IS  - 1541-0420 (Electronic)
IS  - 0006-341X (Print)
IS  - 0006-341X (Linking)
VI  - 73
IP  - 1
DP  - 2017 Mar
TI  - Dynamic models for estimating the effect of HAART on CD4 in observational 
      studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study.
PG  - 294-304
LID - 10.1111/biom.12564 [doi]
AB  - 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.
CI  - (c) 2016, The International Biometric Society.
FAU - Prague, Melanie
AU  - Prague M
AD  - Harvard T.H. Chan School of Public Health, Biostatistics Department, Boston, 
      U.S.A.
FAU - Commenges, Daniel
AU  - Commenges D
AD  - University of Bordeaux, ISPED, F-33000 Bordeaux, France.
AD  - INSERM, U1219 Bordeaux Population Health Research Centre, F-33000, Bordeaux, 
      France.
AD  - INRIA (SISTM) Centre Recherche Bordeaux Sud-Ouest, University of Bordeaux, 
      Talence, France.
FAU - Gran, Jon Michael
AU  - Gran JM
AD  - Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital and 
      University of Oslo, Norway.
FAU - Ledergerber, Bruno
AU  - Ledergerber B
AD  - Division of Infectious Diseases and Hospital Epidemiology, University Hospital of 
      Zurich, Switzerland.
FAU - Young, Jim
AU  - Young J
AD  - Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital 
      of Basel, Switzerland.
FAU - Furrer, Hansjakob
AU  - Furrer H
AD  - Department of Infectious Diseases Bern University Hospital, University of Bern, 
      Switzerland.
FAU - Thiebaut, Rodolphe
AU  - Thiebaut R
AD  - University of Bordeaux, ISPED, F-33000 Bordeaux, France.
AD  - INSERM, U1219 Bordeaux Population Health Research Centre, F-33000, Bordeaux, 
      France.
AD  - INRIA (SISTM) Centre Recherche Bordeaux Sud-Ouest, University of Bordeaux, 
      Talence, France.
LA  - eng
GR  - R37 AI051164/AI/NIAID NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20160726
PL  - England
TA  - Biometrics
JT  - Biometrics
JID - 0370625
RN  - 0 (Anti-HIV Agents)
SB  - IM
MH  - Anti-HIV Agents/*pharmacology
MH  - *Antiretroviral Therapy, Highly Active
MH  - *CD4 Lymphocyte Count
MH  - *Causality
MH  - Cohort Studies
MH  - Computer Simulation
MH  - Humans
MH  - *Linear Models
MH  - Observational Studies as Topic
MH  - Treatment Outcome
MH  - Viral Load
PMC - PMC5269533
MID - NIHMS806270
OTO - NOTNLM
OT  - Dynamic mechanistic models
OT  - Linear increment models (LIM)
OT  - Marginal structural models (MSM)
OT  - Non-linear mixed effect models (NLME)
OT  - Observational study
OT  - Ordinary differential equation (ODE)
EDAT- 2016/07/28 06:00
MHDA- 2017/09/28 06:00
PMCR- 2017/03/24
CRDT- 2016/07/28 06:00
PHST- 2015/07/01 00:00 [received]
PHST- 2016/05/01 00:00 [revised]
PHST- 2016/06/01 00:00 [accepted]
PHST- 2016/07/28 06:00 [pubmed]
PHST- 2017/09/28 06:00 [medline]
PHST- 2016/07/28 06:00 [entrez]
PHST- 2017/03/24 00:00 [pmc-release]
AID - 10.1111/biom.12564 [doi]
PST - ppublish
SO  - Biometrics. 2017 Mar;73(1):294-304. doi: 10.1111/biom.12564. Epub 2016 Jul 26.