Pam,
Please let me know what you discover. I just started looking at a similar
problem. I understand
that a Kalman filter can sometimes be applied to this problem,
but at this time I don't know how to accomplish this.
John
John David Sorkin M.D., Ph.D.
Professor of Medicine
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology and Geriatric
Medicine
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)
>>> Pam Dopart <dopar...@gmail.com> 2/17/2015 4:02 PM >>>
Hello!
I have a longitudinal dataset of radiation exposures of an occupational
cohort. A percentage of the exposure values are missing and I would like to
multiply impute the missing values (it is one option of several we are
comparing). The data are recorded in long format (one row for each exposure
entry) and there are multiple exposure measurements per worker. However,
the data are time-unstructured (different data collection schedules for
each worker) and unbalanced.
I want to account for the correlation between repeated measurements on the
same worker. However, because of the time-unstructured nature of the
dataset, I am unable to convert my dataset into wide format and impute that
way. I have begun reading about about using multilevel imputation for such
a scenario, but I rather unfamiliar with this approach, including within R.
Is this an appropriate method to investigate?
Any advice on how to get started would be greatly appreciated!
Thank you!
Pam
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