Qian Receives NIH Funding to Investigate New Statistical Methods to Study Alzheimer’s Disease Risk Factors

UMass Amherst: News Archive

Biostatistician Jing Qian, biostatistics and epidemiology, has received a two-year, $448,800 exploratory grant from the National Institutes of Health to investigate, with collaborators at Massachusetts General Hospital and Harvard University, new statistical methods for use in regression analysis to explore risk factors in Alzheimer’s disease (AD).In particular, Qian and colleagues will address a statistical problem brought up by incomplete family history. Their new approach is intended to better handle certain variables in AD studies.
The outcome of interest is beta amyloid deposition, which is associated with cognitive decline and is a neuropathological hallmark of AD, as a measure of severity of dementia in cognitively normal older adults who had a parent with Alzheimer’s. “We want to evaluate the contribution of parental history of dementia on this outcome. To do this, we will use regression analysis to assess the relationship between covariates, that is, multiple explanatory variables including the parental history of dementia, and the one outcome variable,” he says.
A major statistical problem encountered in using regression analysis with such a data set is called “randomly censored covariates,” Qian adds. In this case, “censored” means incomplete. For example, for some subjects, their parent has not experienced dementia onset at the time of the child’s interview, so the parent’s age at onset is not known exactly, but the onset age is known to be greater than his or her age at the interview time. Qian and colleagues will explore the proper way to analyze such incomplete data.
In this situation where the precise value of an important explanatory variable is not known but a range can be identified, the researchers propose two “threshold” methods for regression models with covariate subject to random censoring. The first approach is called “deletion threshold regression,” and the second is called “complete threshold regression.” The researchers plan to verify the theoretical properties ...

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