title: A hot-deck multiple imputation procedure for gaps in longitudinal recurrent event histories.
abstract: We propose a regression-based hot-deck multiple imputation method for gaps of missing data in longitudinal studies, where subjects experience a recurrent event process and a terminal event. Examples are repeated asthma episodes and death, or menstrual periods and menopause, as in our motivating application. Research interest concerns the onset time of a marker event, defined by the recurrent event process, or the duration from this marker event to the final event. Gaps in the recorded event history make it difficult to determine the onset time of the marker event, and hence, the duration from onset to the final event. Simple approaches such as jumping gap times or dropping cases with gaps have obvious limitations. We propose a procedure for imputing information in the gaps by substituting information in the gap from a matched individual with a completely recorded history in the corresponding interval. Predictive mean matching is used to incorporate information on longitudinal characteristics of the repeated process and the final event time. Multiple imputation is used to propagate imputation uncertainty. The procedure is applied to an important data set for assessing the timing and duration of the menopausal transition. The performance of the proposed method is assessed by a simulation study.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA. cnwang@umich.edu
authors.forename: Chia-Ning
authors.initials: CN
authors.lastname: Wang
authors.forename: Roderick
authors.initials: R
authors.lastname: Little
authors.forename: Bin
authors.initials: B
authors.lastname: Nan
authors.forename: Siobán D
authors.initials: SD
authors.lastname: Harlow
mesh_terms: D000328:Adult; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008499:Medical Records; D008597:Menstrual Cycle / Q000502:physiology*; D008875:Middle Aged; D015233:Models, Statistical; D047648:Perimenopause / Q000097:blood / Q000502:physiology*; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D013997:Time Factors