Models for residual time to AIDS.
The distributions of the time from Human Immunodeficiency Virus (HIV) infection to the onset of Acquired Immune Deficiency Syndrome (AIDS) and of the residual time to AIDS diagnosis are important for modeling the growth of the AIDS epidemic and for predicting onset of the disease in an individual. Markers such as CD4 counts carry valuable information about disease progression and therefore about the two survival distributions. Building on the framework set out by Jewell and Kalbfleisch (1992), we study these two survival distributions based on stochastic models for the marker process (X(t)) and a marker-dependent hazard (h(.)). We examine various plausible CD4 marker processes and marker-dependent hazard functions for AIDS proposed in recent literature. For a random effects plus Brownian motion marker process X(t) = (a + bt + BM(t))4, where a has a normal distribution, b < 0 is an unknown parameter and BM(t) is Brownian motion, and marker-dependent hazard h(X(t)), we prove that, given CD4 cell count X(t), the residual time to AIDS distribution does not depend on the time since infection t. Using simulation and numerical integration, we find the marginal incubation period distribution, the marginal hazard and the residual time distribution for several combinations of marker processes and marker-dependent hazards. An example using data from the Multicenter AIDS Cohort Study is given. A simple regression model relating the cube root of residual time to AIDS to CD4 count is suggested.
Shi
M
M
Department of Biostatistics, UCLA School of Public Health 90095, USA. mshi@sunlab.ph.ucla.edu
Taylor
J M
JM
Muñoz
A
A
D000163:Acquired Immunodeficiency Syndrome / Q000453:epidemiology / Q000209:etiology* / Q000401:mortality; D015415:Biomarkers; D018791:CD4 Lymphocyte Count; D004196:Disease Outbreaks; D006801:Humans; D015993:Life Tables; D008954:Models, Biological; D016016:Proportional Hazards Models; D016019:Survival Analysis; D013997:Time Factors
D015415:Biomarkers