Multi-state Markov models for analysing incomplete disease history data with illustrations for HIV disease.
Multi-state Markov models can be useful in analysing disease history data. We apply the general estimation methods of Kalbfleisch and Lawless to panel data in which individuals are viewed over only a portion of their life history and complete information about transition times between states is unavailable. Methods to assess goodness-of-fit are proposed. To illustrate the methods, we consider models of HIV disease relating important immunological marker measurements to the onset of AIDS.
Gentleman
R C
RC
Department of Mathematics and Statistics, University of Auckland, New Zealand.
Lawless
J F
JF
Lindsey
J C
JC
Yan
P
P
D000163:Acquired Immunodeficiency Syndrome / Q000276:immunology*; D000328:Adult; D015415:Biomarkers; D001727:Bisexuality; D015496:CD4-Positive T-Lymphocytes / Q000276:immunology*; D005500:Follow-Up Studies; D015658:HIV Infections / Q000276:immunology*; D006679:HIV Seropositivity / Q000276:immunology; D006716:Homosexuality; D006801:Humans; D007070:Immunoglobulin A / Q000032:analysis*; D007958:Leukocyte Count; D008390:Markov Chains; D008487:Medical History Taking / Q000706:statistics & numerical data*; D015233:Models, Statistical
D015415:Biomarkers; D007070:Immunoglobulin A