\documentclass[10pt]{book}
\usepackage{array}
\textwidth=31.9pc
\textheight=46.5pc
\oddsidemargin=1pc
\evensidemargin=1pc
\headsep=15pt
%\headheight=.2cm
\topmargin=.6cm
\parindent=1.7pc
\parskip=0pt
\def\mbf#1{\mbox{\boldmath $#1$}}
\def\author{\centerline}

\setcounter{page}{1}
\begin{document}
\pagestyle{empty}

\footnotesize \noindent Statistica Sinica {\bf 24} (2014), 121-146

\footnotesize \noindent

\vspace{3pc} \normalsize \centerline{\large\bf SEMIPARAMETRIC
ESTIMATION OF TREATMENT} \vspace{2pt} \centerline{\large\bf EFFECTS
IN TWO SAMPLE PROBLEMS} \vspace{2pt} \centerline{\large\bf WITH
CENSORED DATA}   \vspace{.25cm}
\author{Fangfang Bai$^{1,2}$, Jian Huang$^{3}$ and Yong Zhou$^{2,4}$}
\vspace{.4cm} \centerline{\it $^1$University of International
Business and Economics,} \centerline{\it  $^2$Chinese Academy of
Sciences, $^3$University of Iowa}\centerline{\it and $^4$Shanghai
University of Finance and Economics} \vspace{.55cm}

 
\begin{quotation}
\noindent {\it Abstract:} The problem of estimating treatment
effects with censored two-sample data is of importance in survival
analysis and has received much attention in the literature. A common
procedure for dealing with censoring is the inverse probability
weighted method. However, this method only uses information from
uncensored data and can  suffer from loss of efficiency. In this
paper, we propose a unified semiparametric estimating equation
approach to estimate various types of treatment effects with
censored data, including the mean difference between two
populations, the difference between two survival times at a given
point, the probability that the survival time from one population is
greater than that from the other, and the difference in mean
residual life times, among others. Our approach uses all the
available data, thus it typically leads to gains in efficiency as
compared with the existing methods. We study the theoretical
properties  of the proposed estimator and derive its consistent
variance estimator. Our simulation studies demonstrate that the
proposed method tends to work better than the existing ones in
finite sample settings. We also analyze a data set to illustrate its
application.\par \vspace{9pt} \noindent {\it Key words and phrases:}
Treatment effect, semiparametric model, estimating equation,
censored data, two sample problem.
\par
\end{quotation}

\end{document}
