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 C1s 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. Copyright © 2001 John Wiley & Sons, Ltd.

Rossi, C. (2001). 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. STATISTICS IN MEDICINE, 20(24), 3667-3676 [10.1002/sim.1080].

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

ROSSI, CARLA
2001-01-01

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 C1s 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. Copyright © 2001 John Wiley & Sons, Ltd.
2001
Pubblicato
Rilevanza internazionale
Articolo
Sì, ma tipo non specificato
Settore MED/01 - STATISTICA MEDICA
English
Senza Impact Factor ISI
biological marker; CD4 antigen; acquired immune deficiency syndrome; analytical error; article; controlled study; data analysis; disease course; hemophilia; human; Human immunodeficiency virus 1; Human immunodeficiency virus infection; incubation time; intermethod comparison; Kaplan Meier method; lymphocyte count; male; prediction; probability; seroconversion; simulation; statistical model; CD4 Lymphocyte Count; CD4-Positive T-Lymphocytes; Cohort Studies; Computer Simulation; Disease Progression; Greece; Hemophilia A; HIV Infections; HIV-1; Humans; Male; Markov Chains; Models, Immunological
Rossi, C. (2001). 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. STATISTICS IN MEDICINE, 20(24), 3667-3676 [10.1002/sim.1080].
Rossi, C
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/49646
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