Frailty modeling under a selective sampling protocol : an application to type 1 diabetes related autoantibodies
Nevalainen, Jaakko; Datta, Somnath; Toppari, Jorma; Ilonen, Jorma; Hyöty, Heikki; Veijola, Riitta; Knip, Mikael; Virtanen, Suvi M. (2021-09-08)
Nevalainen J, Datta S, Toppari J, et al. Frailty modeling under a selective sampling protocol: an application to type 1 diabetes related autoantibodies. Statistics in Medicine. 2021;40(28):6410-6420.doi: 10.1002/sim.9190
© 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided theoriginal work is properly cited.
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe2022013111544
Tiivistelmä
Abstract
In studies following selective sampling protocols for secondary outcomes, conventional analyses regarding their appearance could provide misguided information. In the large type 1 diabetes prevention and prediction (DIPP) cohort study monitoring type 1 diabetes-associated autoantibodies, we propose to model their appearance via a multivariate frailty model, which incorporates a correlation component that is important for unbiased estimation of the baseline hazards under the selective sampling mechanism. As further advantages, the frailty model allows for systematic evaluation of the association and the differences in regression parameters among the autoantibodies. We demonstrate the properties of the model by a simulation study and the analysis of the autoantibodies and their association with background factors in the DIPP study, in which we found that high genetic risk is associated with the appearance of all the autoantibodies, whereas the association with sex and urban municipality was evident for IA-2A and IAA autoantibodies.
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