\documentclass[12pt]{article}

\usepackage{fullpage,color}

\definecolor{grey}{gray}{.75}
% \definecolor{orange}{cmyk}{0,1,1,0}
\definecolor{orange}{rgb}{.9,.6,.2}

\newcommand{\red}[1]{\textcolor{red}{{#1}}}
\newcommand{\orange}[1]{\textcolor{orange}{{#1}}}
\newcommand{\blue}[1]{\textcolor{blue}{{#1}}}
\newcommand{\green}[1]{\textcolor{green}{{#1}}}


\frenchspacing

\begin{document}

\long\def\thing{

The basic MMTM extends this \green{impairment} model, through joint
modeling, to address the critical issue of survivorship.  Joint
modeling allows us to incorporate the information provided by \red{survival}
times to better reconstruct impairment trajectories---and
vice-versa---and to address the complications derived from \red{attrition
due to death} [3], which is strongly associated with the presence of
HAND, at least prior to the use of cART.

Following [2] and [3, Chapter 6], we assume that each canonical
profile comprises both an \blue{idealized trajectory to severe
  \green{impairment} 
and an idealized \red{mortality} distribution}.  In addition, we assume
that, given an individual's membership vector, $\mathbf{g_i}$,
\blue{survival times and \green{cognitive classification} are independent}.
Thus, we introduce a survival time $s_i$ for each individual $i$, and
expand the trajectory model for that individual to be
\begin{equation}\label{eq:one}
p(y_i,s_i|Age, \mathbf{g_i}) = \left[\sum^K_{k=1}
  g_{ik}d_k(y_i|Age)\right] \left[\sum^K_{k=1}g_{ik}h_k(s_i)\right],
\end{equation}
where $h_k(s_i)$ is the density of the survival distribution for the
$k$-th canonical profile, and the outcome is censored if $Age > s_i$.
\orange{We deal with the problem of right censoring---people who do not
survive long enough to determine their final cognitive
disposition---through a data-augmentation scheme at the time of
estimation via Markov Chain Monte Carlo Simulation.}

The assumption that \blue{survival times and impairment} are independent,
sometimes referred to as local independence, is common and
well-studied in factor analysis models, latent class models, and
latent variable modeling generally [1].  Impairment and survival are
dependent in the observed data, but the assumption of local
independence forces $\mathbf{g_i}$ to be rich enough to fully explain
this dependence when we condtion on it.  

}



{
  \let\red\relax
  \let\orange\relax
  \let\green\relax
  \let\blue\relax

  \thing
}

\clearpage


\thing

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\end{document}
