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\footnotesize \noindent Statistica Sinica {\bf 22} (2012), 489-508

\footnotesize \noindent doi:http://dx.doi.org/10.5705/ss.2010.228

\vspace{3pc} \normalsize \centerline{\large\bf ANALYSIS OF COHORT
SURVIVAL DATA} \vspace{2pt} \centerline{\large\bf  WITH
TRANSFORMATION
 MODEL} \vspace{.25cm}
\author{Kani Chen,  Liuquan Sun  and   Xingwei Tong}
\vspace{.4cm} \centerline{\it Hong Kong University of Science and
Technology,} \centerline{\it  Chinese Academy of Sciences and
Beijing Normal University} \vspace{.55cm} 
\begin{quotation}
\noindent {\it Abstract:} Existing methods for analysis of survival
data arising from cohort sampling
 are largely based on Cox's model and pertained to a certain type of sampling
 design.  This paper applies the general linear transformation model, which
 includes Cox's model and proportional odds model as special cases,
 to a class of  sampling designs including nested case-control,
 case-cohort and classical case-control designs. A simple
 likelihood-based method is developed, and the resulting estimator of the
 regression coefficient is shown to be consistent and asymptotic
 normal. The computation and inference procedures are
 straightforward. In addition to the simplicity and generality
 of the method,  it also has minimal loss of efficiency as the
 observations with missing covariates that are not used
 contain little information about the regression parameter.
 The proposed estimation performs well in simulation
 studies and is applied to analyze the Colorado Plateau uranium
 miners cohort data.\par
\vspace{9pt} \noindent {\it Key words and phrases:} Complete case
analysis, generalized case-cohort sampling, linear transformation
model, missing at random. \par
\end{quotation}

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