Learning Through Chain Event Graphs: The Role of Maternal Factors in Childhood Type 1 Diabetes
| Autor: | Claire Keeble, Graham R. Law, Roger C Parslow, Stuart Barber, Peter A. Thwaites, Paul D. Baxter |
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| Rok vydání: | 2017 |
| Předmět: |
Epidemiology
Mothers 02 engineering and technology 01 natural sciences 010104 statistics & probability Pregnancy Risk Factors 0202 electrical engineering electronic engineering information engineering medicine Humans 0101 mathematics Child Event (probability theory) Type 1 diabetes Models Statistical Rh-Hr Blood-Group System medicine.diagnostic_test Cesarean Section business.industry Case-control study Bayes Theorem Statistical model Delivery Obstetric medicine.disease Missing data United Kingdom Diabetes Mellitus Type 1 Logistic Models Case-Control Studies Prenatal Exposure Delayed Effects Amniocentesis Educational Status Female 020201 artificial intelligence & image processing business Maternal Age Demography Cohort study |
| Zdroj: | American Journal of Epidemiology. 186:1204-1208 |
| ISSN: | 1476-6256 0002-9262 |
| DOI: | 10.1093/aje/kwx171 |
| Popis: | Chain event graphs (CEGs) are a graphical representation of a statistical model derived from event trees. They have previously been applied to cohort studies but not to case-control studies. In this paper, we apply the CEG framework to a Yorkshire, United Kingdom, case-control study of childhood type 1 diabetes (1993-1994) in order to examine 4 exposure variables associated with the mother, 3 of which are fully observed (her school-leaving-age, amniocenteses during pregnancy, and delivery type) and 1 with missing values (her rhesus factor), while incorporating previous type 1 diabetes knowledge. We conclude that the unknown rhesus factor values were likely to be missing not at random and were mainly rhesus-positive. The mother's school-leaving-age and rhesus factor were not associated with the diabetes status of the child, whereas having at least 1 amniocentesis procedure and, to a lesser extent, birth by cesarean delivery were associated; the combination of both procedures further increased the probability of diabetes. This application of CEGs to case-control data allows for the inclusion of missing data and prior knowledge, while investigating associations in the data. Communication of the analysis with the clinical expert is more straightforward than with traditional modeling, and this approach can be applied retrospectively or when assumptions for traditional analyses are not held. |
| Databáze: | OpenAIRE |
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