Causality on longitudinal data: Stable specification search in constrained structural equation modeling

Rahmadi, R., Groot, P., van Rijn, M. H., van den Brand, J. A., Heins, M., Knoop, H., Heskes, T. and OPTIMISTIC Consortium, (2018) Causality on longitudinal data: Stable specification search in constrained structural equation modeling. Statistical Methods in Medical Research, 27(12), pp. 3814-3834. (doi: 10.1177/0962280217713347) (PMID:28657454) (PMCID:PMC6249641)

Abstract

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.

• Item Type: Articles
• Additional Information: This work was supported, in part, by the DGHE of Indonesia as well as by the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 305697. The collection and sharing of brain imaging data used in one of the applications to real-world data was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai, Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research and Development, LLC.; Johnson and Johnson Pharmaceutical Research and Development LLC.; Lumosity; Lundbeck; Merck and Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer, Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The Grantee Organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Monckton, Professor Darren
• Authors: Rahmadi, R., Groot, P., van Rijn, M. H., van den Brand, J. A., Heins, M., Knoop, H., Heskes, T., and OPTIMISTIC Consortium,
• College/School: College of Medical Veterinary and Life Sciences > School of Molecular Biosciences
• Journal Name: Statistical Methods in Medical Research
• Publisher: SAGE
• ISSN: 0962-2802
• ISSN (Online): 1477-0334
• Published Online: 28 June 2018
• Copyright Holders: Copyright © 2017 The Authors
• First Published: First published in Statistical Methods in Medical Research 27(12):3814-3834
• Publisher Policy: Reproduced under a Creative Commons License