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Schauberger G, Tanaka LF, Berger M. A tree-based modeling approach for matched case-control studies. Stat Med 2023; 42:676-692. [PMID: 36631256 DOI: 10.1002/sim.9637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 10/10/2022] [Accepted: 12/14/2022] [Indexed: 01/13/2023]
Abstract
Conditional logistic regression (CLR) is the indisputable standard method for the analysis of matched case-control studies. However, CLR is strongly restricted with respect to the inclusion of non-linear effects and interactions of confounding variables. A novel tree-based modeling method is proposed which accounts for this issue and provides a flexible framework allowing for a more complex confounding structure. The proposed machine learning model is fitted within the framework of CLR and, therefore, allows to account for the matched strata in the data. A simulation study demonstrates the efficacy of the method. Furthermore, for illustration the method is applied to a matched case-control study on cervical cancer.
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Affiliation(s)
- Gunther Schauberger
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| | - Luana Fiengo Tanaka
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| | - Moritz Berger
- Institute of Biomedical Statistics, Computer Science and Epidemiology, University of Bonn, Bonn, Germany
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Skrondal A, Rabe-Hesketh S. The Role of Conditional Likelihoods in Latent Variable Modeling. PSYCHOMETRIKA 2022; 87:799-834. [PMID: 35006532 PMCID: PMC9433368 DOI: 10.1007/s11336-021-09816-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 10/03/2021] [Indexed: 06/14/2023]
Abstract
In psychometrics, the canonical use of conditional likelihoods is for the Rasch model in measurement. Whilst not disputing the utility of conditional likelihoods in measurement, we examine a broader class of problems in psychometrics that can be addressed via conditional likelihoods. Specifically, we consider cluster-level endogeneity where the standard assumption that observed explanatory variables are independent from latent variables is violated. Here, "cluster" refers to the entity characterized by latent variables or random effects, such as individuals in measurement models or schools in multilevel models and "unit" refers to the elementary entity such as an item in measurement. Cluster-level endogeneity problems can arise in a number of settings, including unobserved confounding of causal effects, measurement error, retrospective sampling, informative cluster sizes, missing data, and heteroskedasticity. Severely inconsistent estimation can result if these challenges are ignored.
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Affiliation(s)
- Anders Skrondal
- CEFH, Norwegian Institute of Public Health, P.O.Box 222 Skøyen, N-0213 Oslo, Norway.
- CEMO, University of Oslo, Oslo, Norway.
- GSE, University of California, Berkeley, Berkeley, USA.
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Mollayeva T, Tran A, Chan V, Colantonio A, Sutton M, Escobar MD. Decoding health status transitions of over 200 000 patients with traumatic brain injury from preceding injury to the injury event. Sci Rep 2022; 12:5584. [PMID: 35379824 PMCID: PMC8980052 DOI: 10.1038/s41598-022-08782-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 02/28/2022] [Indexed: 11/17/2022] Open
Abstract
For centuries, the study of traumatic brain injury (TBI) has been centred on historical observation and analyses of personal, social, and environmental processes, which have been examined separately. Today, computation implementation and vast patient data repositories can enable a concurrent analysis of personal, social, and environmental processes, providing insight into changes in health status transitions over time. We applied computational and data visualization techniques to categorize decade-long health records of 235,003 patients with TBI in Canada, from preceding injury to the injury event itself. Our results highlighted that health status transition patterns in TBI emerged along with the projection of comorbidity where many disorders, social and environmental adversities preceding injury are reflected in external causes of injury and injury severity. The strongest associations between health status preceding TBI and health status at the injury event were between multiple body system pathology and advanced age-related brain pathology networks. The interwoven aspects of health status on a time continuum can influence post-injury trajectories and should be considered in TBI risk analysis to improve prevention, diagnosis, and care.
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Mollayeva T, Tran A, Chan V, Colantonio A, Escobar MD. Sex-specific analysis of traumatic brain injury events: applying computational and data visualization techniques to inform prevention and management. BMC Med Res Methodol 2022; 22:30. [PMID: 35094688 PMCID: PMC8802441 DOI: 10.1186/s12874-021-01493-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 11/29/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The interplay of host, agent, and environment implicated in traumatic brain injury (TBI) events is difficult to account for in hypothesis-driven research. Data-driven analysis of injury data can enable insight into injury events in novel ways. This research dissected complex and multidimensional data at the time of the TBI event by exploiting data mining and information visualization methods. METHODS We drew upon population-based decade-long health administrative data collected through the routine operation of the publicly funded health system in Ontario, Canada. We applied a computational approach to categorize health records of 235,003 patients with TBI versus the same number of reference patients without TBI, individually matched based on sex, age, place of residence, and neighbourhood income quantile. We adopted the basic concepts of the Haddon Matrix (host, agent, environment) to organize emerging factors significantly related to TBI versus non-TBI events. To explore sex differences, the data of male and female patients with TBI were plotted on heatmaps and clustered using hierarchical clustering algorithms. RESULTS Based on detected similarities, the computational technique yielded 34 factors on which individual TBI-event codes were loaded, allowing observation of a set of definable patterns within the host, the agent, and the environment. Differences in the patterns of host, agent and environment were found between male and female patients with TBI, which are currently not identified based on data from injury surveillance databases. The results were internally validated. CONCLUSIONS The study outlines novel areas for research relevant to TBI and offers insight into how computational and visual techniques can be applied to advance the understanding of TBI event. Results highlight unique aspects of sex differences of the host and agent at the injury event, as well as differences in exposure to adverse social and environmental circumstances, which can be a function of gender, aiding in future studies of injury prevention and gender-transformative care.
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Affiliation(s)
- Tatyana Mollayeva
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario Canada
- Department of Occupational Science & Occupational Therapy, University of Toronto, Toronto, Ontario Canada
- Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario Canada
- Acquired Brain Injury Research Lab, University of Toronto, Toronto, Ontario Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada Ontario
- Trinity College Institute of Neuroscience, Global Brain Health Institute, Dublin, Ireland
| | - Andrew Tran
- Acquired Brain Injury Research Lab, University of Toronto, Toronto, Ontario Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada Ontario
| | - Vincy Chan
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario Canada
- Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario Canada
- Acquired Brain Injury Research Lab, University of Toronto, Toronto, Ontario Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario Canada
| | - Angela Colantonio
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario Canada
- Department of Occupational Science & Occupational Therapy, University of Toronto, Toronto, Ontario Canada
- Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario Canada
- Acquired Brain Injury Research Lab, University of Toronto, Toronto, Ontario Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada Ontario
| | - Michael D. Escobar
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada Ontario
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