Random effects models for HIV marker data: practical approaches with currently available software.
The analysis of marker data from HIV positive patients has been the motivation for many new developments in applied statistics. As well as reviewing these methods, this paper considers the extent to which programs to implement them are available in current software. Particular areas of development have been the joint modelling of markers and survival outcomes, non-linear random effects models that are of particular relevance for studying the efficacy of treatments and the use of Bayesian computational methods for inference from marker data. The package WinBUGS is recommended as being particularly well suited to the analysis of marker data.
Raab
G M
GM
School of Mathematics and Statistics, Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK.
Parpia
T
T
D015415:Biomarkers; D001699:Biometry; D018791:CD4 Lymphocyte Count; D003627:Data Interpretation, Statistical; D015658:HIV Infections / Q000276:immunology / Q000401:mortality / Q000628:therapy; D006801:Humans; D016014:Linear Models; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D012984:Software; D016019:Survival Analysis
D015415:Biomarkers