Joint modeling of missing data due to non-participation and death in longitudinal aging studies.

Specific age-related hypotheses are tested in population-based longitudinal studies. At specific time intervals, both the outcomes of interest and the time-varying covariates are measured. When participants are approached for follow-up, some participants do not provide data. Investigations may show that many have died before the time of follow-up whereas others refused to participate. Some of these non-participants do not provide data at later follow-ups. Few statistical methods for missing data distinguish between 'non-participation' and 'death' among study participants. The augmented inverse probability-weighted estimators are most commonly used in marginal structure models when data are missing at random. Treating non-participation and death as the same, however, may lead to biased estimates and invalid inferences. To overcome this limitation, a multiple inverse probability-weighted approach is presented to account for two types of missing data, non-participation and death, when using a marginal mean model. Under certain conditions, the multiple weighted estimators are consistent and asymptotically normal. Simulation studies will be used to study the finite sample efficiency of the multiple weighted estimators. The proposed method will be applied to study the risk factors associated with the cognitive decline among the aging adults, using data from the Chicago Health and Aging Project (CHAP).