The Nun Study, a longitudinal study to examine risk factors for the progression of dementia, consists of subjects who were already diagnosed with dementia (ie, prevalent cohort) and those who do not have dementia (ie, incident cohort) at study enrollment. When assessing the risk factors' effects on the survival time from dementia diagnosis until death, utilizing data from both cohorts supports more efficient statistical inference because the two cohorts provide valuable complementary information. A major challenge in analyzing the combined cohort data is that the prevalent cases are not representative of the target population. Moreover, the dates of dementia diagnosis are not ascertained for the prevalent cohort in the Nun Study. Hence, the survival time for the prevalent cohort is only partially observed from study enrollment until death or censoring, with the time from dementia diagnosis to study enrollment missing. In this paper, we propose an efficient estimation method that uses both incident and prevalent cohorts under the proportional mean residual life model. By assuming proportionality of the mean residual life time with covariates in the incident cohort, we can utilize the natural relationship between the mean residual life function and the hazard function of the survival time measured from enrollment until death for the prevalent cohort. We evaluate the efficiency gain from using the combined cohort data through simulations and demonstrate that the proposed method is valid and efficient.
|Number of pages||12|
|Journal||Statistics in Medicine|
|State||Published - May 30 2019|
Bibliographical noteFunding Information:
This work was partially supported by the US National Institutes of Health through grants CA193878 and CA016672. The Nun Study data reported in this article were collected from the SMART project (AG386561). The authors also acknowledge the Texas Advanced Computing Center at The University of Texas at Austin for providing HPC resources that contributed to the research results reported within this paper.
© 2019 John Wiley & Sons, Ltd.
- Nun Study
- combined cohort data
- incident cohort
- prevalent cohort
- proportional hazards model
- proportional mean residual life model
ASJC Scopus subject areas
- Statistics and Probability