Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Murphy says mobile sensor data will allow adaptive interventions for maximizing healthy outcomes

Frey comments on why sunbelt metro area economies are still struggling

Krause says having religious friends leads to gratitude, which is associated with better health

Highlights

PSC Fall 2014 Newsletter now available

Martha Bailey and Nicolas Duquette win Cole Prize for article on War on Poverty

Michigan's graduate sociology program tied for 4th with Stanford in USN&WR rankings

Jeff Morenoff makes Reuters' Highly Cited Researchers list for 2014

Next Brown Bag

Monday, Oct 20
No brown bag this week

A hot-deck multiple imputation procedure for gaps in longitudinal data on recurrent events

Publication Abstract

Little, R.J., M. Yosef, K.C. Cain, B. Nan, and Sioban D. Harlow. 2008. "A hot-deck multiple imputation procedure for gaps in longitudinal data on recurrent events." Statistics in Medicine, 27(1): 103-120.

Hot-deck imputation offers advantages in reflecting salient features of data distributions in missing-data problems, but previous implementations have lacked the appeal associated with modern Bayesian statistical-computing techniques. We outline a strategy of iterative hot-deck multiple imputation with distance-based donor selection. With distance defined as a monotonic function of the difference in predictive means between cases, donors are chosen with probability inversely proportional to their distance from the donee. This method retains the implementation ease of ad hoc techniques, while incorporating the desirable features of Bayesian approaches. Special cases of our method include nearest-neighbor imputation and a simple random hot-deck. Iterating the procedure provides an analogy to Markov Chain Monte Carlo methods and is intended to mitigate dependence on starting values. Results from imputing missing values in a longitudinal depression treatment trial as well as a simulation study are presented. We evaluate how different definitions of distance, choices of starting values, the order in which variables are chosen for imputation, and the number of iterations impact inferences. We show that our measure of distance controls the tradeoff between bias and variance of our estimates. We find that inferences from the depression treatment trial are not sensitive to most definitions of distance. In addition, while differences exist between 1 iteration and 10 iterations, there are no meaningful differences between inferences based on 10 iterations and those based on 500 iterations. The choice of starting value did not have an impact on inferences but the order in which the variables were chosen for imputation was significant even after iteration.

DOI:10.1002/sim.3001 (Full Text)

Country of focus: United States of America.

Browse | Search : All Pubs | Next