The one-sample problem from eradication studies of chronic viral infections

In studies of chronic viral infections, the objective is to estimate probab ilities of developing viral eradication and resistance. Complications arise as the laboratory methods used to assess eradication status result in unus ual types of censored observations. This paper proposes nonparametric metho ds for the one-sample analysis of viral eradication/resistance data. We sho w that the unconstrained nonparametric maximum likelihood estimator of the subdistributions of eradication and resistance are obtainable in closed ter m. In small samples, these estimators may be inadmissible; thus, we also pr esent an algorithm for obtaining the constrained MLEs based on an isotonic regression of the unconstrained MLEs. Estimators of several functionals of the eradication and resistance subdistributions are also developed and disc ussed. The methods ale illustrated with results from recent hepatitis C cli nical trials.