Stochastic models to demonstrate the effect of motivated testing on HIV incidence estimates using the serological testing algorithm for recent HIV seroconversion (STARHS). - White, Edward W; Lumley, Thomas; Goodreau, Steven M; Goldbaum, Gary; Hawes, Stephen E (Sexually transmitted infections, 2010)
To produce valid seroincidence estimates, the serological testing algorithm for recent HIV seroconversion (STARHS) assumes independence between infection and testing, which may be absent in clinical data. STARHS estimates are generally greater than cohort-based estimates of incidence from observable person-time and diagnosis dates. The authors constructed a series of partial stochastic models to examine whether testing motivated by suspicion of infection could bias STARHS.
Keywords: AIDS Serodiagnosis; Algorithms; Bias (Epidemiology); Enzyme-Linked Immunosorbent Assay; HIV Seropositivity; Homosexuality, Male; Humans; Incidence; Male; Monte Carlo Method; Motivation; Patient Acceptance of Health Care; Random Allocation; Sexual Partners; Stochastic Processes; Time Factors