Abstract Distributed data networks (DDNs) with horizontally partitioned datasets are viable resources for multicenter research studies and pharmacosurveillance. Within DDNs, maintaining confidentiality and limiting the disclosure of sensitive information is critical. Consequently, data sharing between partners within the same network is either restricted or completely prohibited during statistical modeling. Current privacy-preserving methods for logistic regression span two extreme paradigms: meta-analysis (MA), which combines estimates based on partner-specific estimates, is convenient for the analytical center (AC) but requires separate implementations of the analysis by each data node; while distributed regression (DR), which provides overall estimates based on partner-specific data summaries, produces rigorous solutions but is an iterative process that is both time and resource consuming. A practical middle ground that combines the convenience of MA and the rigor of DR is lacking. We propose a likelihood-based approach for logistic regression modeling that combines the rigor of DR and the convenience of MA. The two-stage approach has an equivalent estimation performance as DR but foregoes its multiple iterative steps through an MA update step, and is therefore more user-friendly. The approach uses only aggregate-level covariates to estimate a starting pooled effect estimate and within-node data summaries for a single-shot update of the pooled estimate without requiring individual covariate values at the AC. We call the approach hybrid Pooled Logistic Regression (hPoLoR) and show that it conveniently provides accurate and efficient estimates of the standard individual-level log odds ratios and standard errors without revealing personal data. Hence hPoLoR provides a rigorous yet convenient and application-friendly alternative to MA and DR. The method is demonstrated through extensive simulations and application to the JCUSH data.
Whilst cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers for amyloid-β (Aβ) and tau pathologies are accurate for the diagnosis of Alzheimer's disease (AD), their broad implementation in clinical and trial settings are restricted by high cost and limited accessibility. Plasma phosphorylated-tau181 (p-tau181) is a promising blood-based biomarker that is specific for AD, correlates with cerebral Aβ and tau pathology, and predicts future cognitive decline. In this study, we report the performance of p-tau181 in >1000 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including cognitively unimpaired (CU), mild cognitive impairment (MCI) and AD dementia patients characterized by Aβ PET. We confirmed that plasma p-tau181 is increased at the preclinical stage of Alzheimer and further increases in MCI and AD dementia. Individuals clinically classified as AD dementia but having negative Aβ PET scans show little increase but plasma p-tau181 is increased if CSF Aβ has already changed prior to Aβ PET changes. Despite being a multicenter study, plasma p-tau181 demonstrated high diagnostic accuracy to identify AD dementia (AUC = 85.3%; 95% CI, 81.4-89.2%), as well as to distinguish between Aβ- and Aβ+ individuals along the Alzheimer's continuum (AUC = 76.9%; 95% CI, 74.0-79.8%). Higher baseline concentrations of plasma p-tau181 accurately predicted future dementia and performed comparably to the baseline prediction of CSF p-tau181. Longitudinal measurements of plasma p-tau181 revealed low intra-individual variability, which could be of potential benefit in disease-modifying trials seeking a measurable response to a therapeutic target. This study adds significant weight to the growing body of evidence in the use of plasma p-tau181 as a non-invasive diagnostic and prognostic tool for AD, regardless of clinical stage, which would be of great benefit in clinical practice and a large cost-saving in clinical trial recruitment. ; Data collection and sharing was funded by ADNI (NIH #U01 AG024904) and DOD ADNI (#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; Eisa i 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 & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Ph armaceuticals 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 Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. TKK holds a Brightfocus fellowship (#A2020812F), and is further supported by the Swedish Alzheimer Foundation (Alzheimerfonden; #AF-930627), the Swedish Brain Foundation (Hjärnfonden; #FO2020-0240), the Swedish Dementia Foundation (Demensförbundet), the Agneta Prytz-Folkes & Gösta Folkes Foundation (#2020-00124), the Aina (Ann) Wallströms and Mary-Ann Sjöbloms Foundation, the Anna Lisa and Brother Björnsson's Foundation, Gamla Tjänarinnor, and the Gun and Bertil Stohnes Foundation. NJA is supported by the Swedish Alzheimer Foundation (Alzheimerfonden; #AF-931009), the Swedish Brain Foundation (Hjärnfonden), the Agneta Prytz-Folkes & Gösta Folkes Foundation, and the Swedish Dementia Foundation (Demensförbundet). AS was supported by the Emil Aaltonen Foundation and the Paul o Foundation, and currently receives funding from the Orion Research Foundation. MS-C received funding fro m the European Union's Horizon 2020 Research and Innovation Program under the Marie Skl odowska-Curie action grant agreement No 752310, and currently receives funding from Instituto de Salud Carlos III (PI19/00155) and from the Spanish Ministry of Science, Innovation and Universities (Juan de la Cierva Programme grant IJC2018-037478-I). PR-N is supported by the Weston Brain Institute, the Canadian Institutes of Health Research, the Canadian Consortium on Neurodegeneration in Aging and the Fonds de Recherche du Québec – Santé (FRQS; Chercheur Boursier, and 2020-VICO-279314 TRIAD/BIOVIE Cohort), the CIHR-CCNA Canadian Consortium of Neurodegeneration in Aging, and the Canada Foundation for Innovation (project 34874). KB was supported by the Alzheimer Drug Discovery Foundation (ADDF; #RDAPB- 201809-2016615), the Swedish Research Council (#2017-00915), the Swedish Alzheimer Foundation (#AF-742881), Hjärnfonden, Sweden (#FO2017-0243), and a grant (#ALFGBG-715986) from the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement. KB is supported by the Swedish Research Council (#2017-00915), the Alzheimer Drug Discovery Foundation (ADDF), USA (#RDAPB-201809-2016615), the Swedish Alzheimer Foundation (#AF-742881), Hjärnfonden, Sweden (#FO2017- 0243), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF- agreement (#ALFGBG-715986), and European Union Joint Program for Neurodegenerative Disorders (JPND2019- 466-236). HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018-02532), the European Research Council (#681712), Swedish State Support for Clinical Research (# ALFGBG-720931), the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862), the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), and the UK Dementia Research Institute at UCL.
