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\noindent Statistica Sinica {\bf 16}(2006), 287-302

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\centerline{\large\bf SEMIPARAMETRIC ADDITIVE RISKS MODEL FOR }
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\centerline{\large\bf INTERVAL-CENSORED DATA}
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\author{Donglin Zeng$^1$, Jianwen Cai$^1$ and Yu Shen$^2$}
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\centerline{\it $^1$University of North Carolina at Chapel Hill and
$^2$The University of Texas}
%\centerline{\it $^2$The University of Texas}
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\begin{quotation}
\noindent {\it Abstract:}
Interval-censored event time data often arise in medical and
public health studies. In such a setting, the exact time of the
event of interest cannot be observed and is only known to fall
between two monitoring times. Our interest focuses on the
estimation of the effect of risk factors on interval-censored data
under the semiparametric additive hazards model. A nonparametric
step-function is used to characterize the baseline hazard
function. The covariate coefficients are estimated by maximizing
the observed likelihood function, and their variances are obtained
using the profile likelihood approach. We show that the proposed
estimates are consistent and have asymptotic normal distributions.
We also show that the estimator obtained for the covariate
coefficient is the most efficient estimator.  Simulation studies
are conducted to assess the performance of the estimate. The
method is illustrated through application to a data set from an
HIV study.\par
\vspace{9pt}
\noindent {\it Key words and phrases:}
Additive hazards regression, interval-censored data,
nonparametric maximum likelihood estimates, profile likelihood,
semiparametric efficiency.\par
\end{quotation}

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