<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sypsa, V</style></author><author><style face="normal" font="default" size="100%">Touloumi, G.</style></author><author><style face="normal" font="default" size="100%">Kenward, M.</style></author><author><style face="normal" font="default" size="100%">Karafoulidou, A.</style></author><author><style face="normal" font="default" size="100%">Hatzakis, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">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</style></title><secondary-title><style face="normal" font="default" size="100%">Stat MedStat MedStat Med</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Statistics in medicine</style></alt-title><short-title><style face="normal" font="default" size="100%">Statistics in medicineStatistics in medicine</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Disease Progression</style></keyword><keyword><style  face="normal" font="default" size="100%">*Markov Chains</style></keyword><keyword><style  face="normal" font="default" size="100%">*Models, Immunological</style></keyword><keyword><style  face="normal" font="default" size="100%">CD4 Lymphocyte Count</style></keyword><keyword><style  face="normal" font="default" size="100%">CD4-Positive T-Lymphocytes/cytology/*immunology</style></keyword><keyword><style  face="normal" font="default" size="100%">Cohort Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">computer simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Greece</style></keyword><keyword><style  face="normal" font="default" size="100%">Hemophilia A/complications</style></keyword><keyword><style  face="normal" font="default" size="100%">HIV Infections/complications/*immunology</style></keyword><keyword><style  face="normal" font="default" size="100%">HIV-1/*growth &amp; development</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Dec 30</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">24</style></number><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">3667-76</style></pages><isbn><style face="normal" font="default" size="100%">0277-6715 (Print)0277-6715 (Linking)</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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 &quot;no smoothing&quot; estimate. With the exception of the &quot;no smoothing&quot; method, &quot;ad hoc&quot;, 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.</style></abstract><accession-num><style face="normal" font="default" size="100%">11782025</style></accession-num><notes><style face="normal" font="default" size="100%">Sypsa, VTouloumi, GKenward, MKarafoulidou, AHatzakis, AengComparative StudyEngland2002/01/10 10:00Stat Med. 2001 Dec 30;20(24):3667-76. doi: 10.1002/sim.1080.</style></notes><auth-address><style face="normal" font="default" size="100%">Department of Hygiene &amp; Epidemiology, Athens University Medical School, M. Asias 75, 11527 Athens, Greece. vsipsa@cc.uoa.gr</style></auth-address></record></records></xml>