Article

Comparison of smoothing techniques for CD4 data in a Markov model with states defined by CD4: an example on the estimation of the HIV incubation time distribution

Department of Hygiene & Epidemiology, Athens University Medical School, M. Asias 75, 11527 Athens, Greece.
Statistics in Medicine (Impact Factor: 2.04). 12/2001; 20(24):3667-76. DOI: 10.1002/sim.1080
Source: PubMed

ABSTRACT Multi-state models defined in terms of CD4 counts are useful for modelling HIV disease progression. A Markov model with six progressive CD4-based states and an absorbing state (AIDS) was used to estimate the cumulative probability of progressing to AIDS in 158 HIV-1 infected haemophiliacs with known seroconversion (SC) dates. A problem arising in such analysis is how to define CD4-based states, since this marker is subject to measurement error and short timescale variability. Four approaches were used: no smoothing, ad hoc smoothing (to move to a later/previous state two consecutive measurements to later/previous states are needed), kernel smoothing and random effects (RE) models. The estimates were compared with the Kaplan-Meier estimate based solely on data concerning time to AIDS. There was an apparent lack of agreement between the Kaplan-Meier and the "no smoothing" estimate. With the exception of the "no smoothing" method, "ad hoc", kernel and RE estimates fell within the range of the 95 per cent CIs of the Kaplan-Meier curve. Simulations demonstrated that the use of raw CD4 counts provides overestimated transition intensities. Compared to the kernel method, ad hoc is easier to implement and overcomes the problem of the choice of bandwidth. The RE approach leads to simple models, since it usually results in very few transitions to previous states, and can handle individuals with sparse data by smoothing their predictions towards the population mean. Ad hoc was the method that performed better, in terms of bias, than the other smoothing approaches.

0 Followers
 · 
31 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: The prediction of erosion and co-deposition processes for ITER is necessary information for the design and material choice of the first wall. A model has been developed that describes this coupling of local erosion to the global impurity transport and re-deposition processes in a self-consistent way. The erosion and deposition on each surface element of first wall is described by an ordinary differential equation (ODE). The resulting system of ODEs is coupled via the impurity influx, which is derived from the re-distribution of the erosion fluxes through the global impurity transport as calculated by DIVIMP. As a test case, the model is applied to a standard ITER reference discharge calculating the re-distribution of Be, C and W inside the ITER vessel with time.
    Journal of Nuclear Materials 08/2011; DOI:10.1016/j.jnucmat.2011.01.105 · 2.02 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In AIDS control, physicians have a growing need to use pragmatically useful and interpretable tools in their daily medical taking care of patients. Semi-Markov process seems to be well adapted to model the evolution of HIV-1 infected patients. In this study, we introduce and define a non homogeneous semi-Markov (NHSM) model in continuous time. Then the problem of finding the equations that describe the biological evolution of patient is studied and the interval transition probabilities are computed. A parametric approach is used and the maximum likelihood estimators of the process are given. A Monte Carlo algorithm is presented for realizing non homogeneous semi-Markov trajectories. As results, interval transition probabilities are computed for distinct times and follow-up has an impact on the evolution of patients.
    Methodology And Computing In Applied Probability 08/2007; 9(3):389-397. DOI:10.1007/s11009-007-9033-7 · 0.78 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Accurately estimating rates of disease progression is of central importance in developing mathematical models used to project outcomes and guide resource allocation decisions. Our objective was to specify a multivariate regression model to estimate changes in disease progression among individuals on highly active antiretroviral treatment in British Columbia, Canada, 1996-2011. We used population-level data on disease progression and antiretroviral treatment utilization from the BC HIV Drug Treatment Program. Disease progression was captured using longitudinal CD4 and plasma viral load testing data, linked with data on antiretroviral treatment. The study outcome was categorized into (CD4 count ≥ 500, 500-350, 350-200, <200 cells/mm, and mortality). A 5-state continuous-time Markov model was used to estimate covariate-specific probabilities of CD4 progression, focusing on temporal changes during the study period. A total of 210,083 CD4 measurements among 7421 individuals with HIV/AIDS were included in the study. Results of the multivariate model suggested that current highly active antiretroviral treatment at baseline, lower baseline CD4 (<200 cells/mm), and extended durations of elevated plasma viral load were each associated with accelerated progression. Immunological improvement was accelerated significantly from 2004 onward, with 23% and 46% increases in the probability of CD4 improvement from the fourth CD4 stratum (CD4 < 200) in 2004-2008 and 2008-2011, respectively. Our results demonstrate the impact of innovations in antiretroviral treatment and treatment delivery at the population level. These results can be used to estimate a transition probability matrix flexible to changes in the observed mix of clients in different clinical stages and treatment regimens over time.
    JAIDS Journal of Acquired Immune Deficiency Syndromes 08/2013; 63(5):653-9. DOI:10.1097/QAI.0b013e3182976891 · 4.39 Impact Factor