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Patients at the same stage of chronic disease may have had different rates of disease progression. The authors developed a mathematical modeling approach that allows reconstructing and comparing populations in terms of the disease progression rates of their participants when the disease onset and progression rates are unknown for individual patients. Human immunodeficiency virus 1 infection was used as an example. Both published and hypothetical models were used to describe the human immunodeficiency virus 1 epidemic (epidemic heterogeneity) and incubation and survival functions for different disease stages (individual heterogeneity). Reconstructions of populations with late disease (e.g., acquired immunodeficiency syndrome patients) show a marked predominance of rapid progressors, unless the incidence of new infections has been decreasing for a long time. Rapid progressors would also predominate in populations of acute seroconverters, unless diagnosis is based on repeated serologic screening rather than symptoms. Populations of patients who have not progressed beyond an early stage of the disease (e.g., patients with CD4 cell counts &gt; 500/microliter) tend to overrepresent slow progressors, especially if the epidemic has been decreasing for a long time. With this approach, one can assess whether the target population of a clinical trial is comparable with other patient populations at different places and times. Epidemic and individual diversity may even affect trial results if patients with different progression rates experience different benefits from a treatment. By modeling the targeted populations in trials of early versus deferred antiretroviral treatment, the authors observed larger treatment benefits in trials in which rapid progressors probably predominated, compared with trials of slow progressors.

american journal of epidemiology

Impact of Epidemic and Individual Heterogeneity on the Population Distribution of Disease Progression Rates: An Example from Patient Populations in Trials of Human Immunodeficiency Virus Infection