Observational studyInternational journal of methods in psychiatric research2022
Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency.
Observational study in International journal of methods in psychiatric research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Toxoplasma gondii and suicidal behaviour: discovery, cross-diagnostic confirmation and pathway exploration.Folia parasitologica · 2025Article
- Article
- The association between vitamin D serum levels, supplementation, and suicide attempts and intentional self-harm.PloS one · 2023Article
- Evaluating the heterogeneous effect of a modifiable risk factor on suicide: The case of vitamin D deficiency.International journal of methods in psychiatric research · 2022Observational
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Authors and funding
13 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo illustrate the use of machine learning methods to search for heterogeneous effects of a target modifiable risk factor on suicide in observational studies. The illustration focuses on secondary analysis of a matched case-control study of vitamin D deficiency predicting subsequent suicide.
methodsWe describe a variety of machine learning methods to search for prescriptive predictors; that is, predictors of significant variation in the association between a target risk factor and subsequent suicide. In each case, the purpose is to evaluate the potential value of selective intervention on the target risk factor to prevent the outcome based on the provisional assumption that the target risk factor is causal. The approaches illustrated include risk modeling based on the super learner ensemble machine learning method, Least Absolute Shrinkage and Selection Operator (Lasso) penalized regression, and the causal forest algorithm.
resultsThe logic of estimating heterogeneous intervention effects is exposited along with the illustration of some widely used methods for implementing this logic.
conclusionsIn addition to describing best practices in using the machine learning methods considered here, we close with a discussion of broader design and analysis issues in planning an observational study to investigate heterogeneous effects of a modifiable risk factor.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.