Analysis of time to a silent event whose occurrence is monitored with error, with application to mother-to-child HIV transmission.
Inferences about the distribution of time to HIV infection in infants are complicated because infection is a silent event and imperfect diagnostic tests are used to detect its occurrence, leading to false-positive and false-negative results. Nonparametric likelihood approaches are computationally hampered by a large number of parameters and a possibly nonconcave likelihood function. To overcome these difficulties, we develop one-sample and regression methods based on profile likelihood and Markov chain Monte Carlo techniques. The methods also provide a useful diagnostic for assessing the infection status of individual subjects, and are illustrated using results from a recent clinical trial for the prevention of mother-to-child HIV transmission.
Zhang
Peng
P
Department of Biostatistics, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA.
Lagakos
Stephen W
SW
D000465:Algorithms; D015982:Bias; D003955:Diagnostic Tests, Routine / Q000706:statistics & numerical data; D005260:Female; D015658:HIV Infections / Q000175:diagnosis* / Q000453:epidemiology / Q000635:transmission*; D006801:Humans; D018445:Infectious Disease Transmission, Vertical / Q000706:statistics & numerical data*; D008390:Markov Chains; D009010:Monte Carlo Method; D018709:Statistics, Nonparametric; D013997:Time Factors