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\footnotesize \noindent Statistica Sinica {\bf 21} (2011),1315-1339

\vspace{3pc} \normalsize \centerline{\large\bf TESTING INDEPENDENT
CENSORING} \vspace{2pt} \centerline{\large\bf  FOR LONGITUDINAL
DATA} \vspace{.25cm}
\author{Yanqing Sun and Jimin Lee}
\vspace{.4cm} \centerline{\it University of North Carolina at
Charlotte and}\centerline{\it University of North Carolina at
Asheville} \vspace{.55cm} 
\begin{quotation}
\noindent {\it Abstract:} A common problem associated with
longitudinal studies is the dropouts of subjects or censoring
before the end of follow-up. In most existing methods, it is assumed
that censoring is noninformative about missed responses. This
assumption is crucial to the validity of many statistical
procedures. We develop some nonparametric hypothesis testing
procedures to test for independent censoring in the absence/presence
of covariates. The test statistics are constructed by contrasting
two estimators of the conditional mean of cumulative responses for
each stratum of covariate space from sample subsets with different
patterns of censoring. Our method does not require the modelling of
longitudinal response processes, therefore is robust to model
misspecifications. A diagnostic plot procedure is also developed
that can be used to identify dependent censoring to certain
covariate strata. The finite sample performances of the tests are
investigated through extensive simulation studies. The potential of
our methods is demonstrated through the application of the tests to
a chronic granulomatous disease study.\par \vspace{9pt} \noindent
{\it Key words and phrases:} CGD data, Gaussian multiplier method,
informative censoring, integrated square test,  marginal and
conditional independent censoring, nonparametric tests, recurrent
events,  supremum test, weak convergence. \par
\end{quotation}

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