Chenguang Wang [aut, cre],
Andrew Leroux [aut, cre],
Elizabeth Colantuoni [aut],
Daniel O Scharfstein [aut],
Trustees of Columbia University [cph] (tools/make_cpp.R, R/stanmodels.R)
In randomized studies involving severely ill patients, functional
outcomes are often unobserved due to missed clinic visits, premature
withdrawal or death. It is well known that if these unobserved functional
outcomes are not handled properly, biased treatment comparisons can be
produced. In this package, we implement a procedure for comparing treatments
that is based on the composite endpoint of both the functional outcome and
survival. The procedure was proposed in Wang et al. (2016) <doi:10.1111/biom.12594>.
It considers missing data imputation with a sensitivity
analysis strategy to handle the unobserved functional outcomes not due to
death.
sqldf (>= 0.4), survival (>= 2.38), parallel (>= 3.2), methods
knitr, shiny, rmarkdown, pander, DT, shinythemes

In randomized studies involving severely ill patients, functional
outcomes are often unobserved due to missed clinic visits, premature
withdrawal or death. It is well known that if these unobserved functional
outcomes are not handled properly, biased treatment comparisons can be
produced. In this package, we implement a procedure for comparing treatments
that is based on the composite endpoint of both the functional outcome and
survival. The procedure was proposed in Wang et al. (2016) <DOI:10.1111/biom.12594>.
It considers missing data imputation with a sensitivity
analysis strategy to handle the unobserved functional outcomes not due to
death.

In randomized studies involving severely ill patients, functional
outcomes are often unobserved due to missed clinic visits, premature
withdrawal or death. It is well known that if these unobserved functional
outcomes are not handled properly, biased treatment comparisons can be
produced. In this package, we implement a procedure for comparing treatments
that is based on the composite endpoint of both the functional outcome and
survival. The procedure was proposed in Wang et al. (2016) <DOI:10.1111/biom.12594>
and Wang et al. (2020) <DOI:10.18637/jss.v093.i12>. It considers missing data
imputation with different sensitivity
analysis strategies to handle the unobserved functional outcomes not due to
death.