Rebecca Betensky has received a grant for Statistical methods for censored and dependently truncated data. This 4-year R01 grant was awarded by the National Institute for Neurological Disorders and Stroke (NINDS). Collaborators are Micha Mandel (Hebrew University), Jing Qian (University of Massachusetts), Steven Chiou (HSPH), Bradley Hyman (MGH), and Reisa Sperling (MGH).  Â
Funding will support the following proposal:
Our team is extensively engaged in neurological disease studies, which are often plagued with dependent truncation. We adopt a range of analytical approaches to address dependent truncation that arises through any of several possible mechanisms. We accommodate unexplained dependence through inversion of transformation models and permutation null distributions, nonparametric bounds and estimation, and semi-parametric models, covariate-induced dependence through inverse probability weighting methods, and dependence that is induced by sequential truncating events through copula models. We aim to establish a significantly enhanced collection of usable and robust methods for the analysis of dependently truncated data, which will strengthen the validity of research findings from studies of major public health problems, such as Alzheimer’s disease.
