Robust data-driven identification of risk factors and their interactions : A simulation and a study of parental and demographic risk factors for schizophrenia

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1-11
Journal / PublicationInternational Journal of Methods in Psychiatric Research
Volume29
Issue number4
Online published10 Jun 2020
Publication statusPublished - Dec 2020
Externally publishedYes

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Abstract

Objectives: Few interactions between risk factors for schizophrenia have been replicated, but fitting all such interactions is difficult due to high-dimensionality. Our aims are to examine significant main and interaction effects for schizophrenia and the performance of our approach using simulated data. 
Methods: We apply the machine learning technique elastic net to a high-dimensional logistic regression model to produce a sparse set of predictors, and then assess the significance of odds ratios (OR) with Bonferroni-corrected p-values and confidence intervals (CI). We introduce a simulation model that resembles a Finnish nested case–control study of schizophrenia which uses national registers to identify cases (n = 1,468) and controls (n = 2,975). The predictors include nine sociodemographic factors and all interactions (31 predictors). 
Results: In the simulation, interactions with OR = 3 and prevalence = 4% were identified with <5% false positive rate and ≥80% power. None of the studied interactions were significantly associated with schizophrenia, but main effects of parental psychosis (OR = 5.2, CI 2.9–9.7; p <.001), urbanicity (1.3, 1.1–1.7; p =.001), and paternal age ≥35 (1.3, 1.004–1.6; p =.04) were significant. 
Conclusions: We have provided an analytic pipeline for data-driven identification of main and interaction effects in case–control data. We identified highly replicated main effects for schizophrenia, but no interactions.

Research Area(s)

  • data-driven, epidemiology, interaction, risk factors, schizophrenia

Citation Format(s)

Robust data-driven identification of risk factors and their interactions : A simulation and a study of parental and demographic risk factors for schizophrenia. / Gyllenberg, David; McKeague, Ian W.; Sourander, Andre; Brown, Alan S.

In: International Journal of Methods in Psychiatric Research, Vol. 29, No. 4, 12.2020, p. 1-11.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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