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

PSC In The News

RSS Feed icon

Shaefer and Edin's book ($2 a Day) cited in piece on political debate over plight of impoverished Americans

Eisenberg tracks factors affecting both mental health and athletic/academic performance among college athletes

Shapiro says Americans' low spending reflects "cruel lesson" about the dangers of debt

Highlights

Susan Murphy elected to the National Academy of Sciences

Maggie Levenstein named director of ISR's Inter-university Consortium for Political and Social Research

Arline Geronimus receives 2016 Harold R. Johnson Diversity Service Award

PSC spring 2016 newsletter: Kristin Seefeldt, Brady West, newly funded projects, ISR Runs for Bob, and more

Next Brown Bag

PSC Brown Bags
will resume fall 2016

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