University of Oulu

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

Frailty modeling under a selective sampling protocol : an application to type 1 diabetes related autoantibodies

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Author: Nevalainen, Jaakko1; Datta, Somnath2; Toppari, Jorma3,4;
Organizations: 1Health Sciences, Faculty of Social Sciences, Tampere University, Tampere, Finland
2Department of Biostatistics, University of Florida, Gainesville, Florida
3Institute of Biomedicine, University of Turku, Turku, Finland
4Department of Pediatrics, Turku University Hospital, Turku, Finland
5Faculty of Medicine and Health Technology, Tampere University,Tampere, Finland
6Department of Pediatrics, Oulu University Hospital and University of Oulu, Oulu, Finland
7Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
8Public Health and Welfare Department, Finnish Institute for Health and Welfare, Helsinki, Finland
9Research, Development and Innovation Centre, and Center for Child Health Research, Tampere University and University Hospital, Tampere, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
Persistent link:
Language: English
Published: John Wiley & Sons, 2021
Publish Date: 2022-01-31


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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Series: Statistics in medicine
ISSN: 0277-6715
ISSN-E: 1097-0258
ISSN-L: 0277-6715
Volume: 40
Issue: 28
Pages: 6410 - 6420
DOI: 10.1002/sim.9190
Type of Publication: A1 Journal article – refereed
Field of Science: 3121 General medicine, internal medicine and other clinical medicine
Funding: Foundation for the National Institutes of Health, Grant/Award Numbers:1R03DE026757-01A1, 5R03DE026757-02
Copyright information: © 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.