ArticleGenetic epidemiology2009
Modifications to the Patient Rule-Induction Method that utilize non-additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease.
Article in Genetic epidemiology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed, 9 citations in OpenAlex.
- An Application of the Patient Rule-Induction Method to Detect Clinically Meaningful Subgroups from Failed Phase III Clinical Trials.International journal of clinical biostatistics and biometrics · 2021Article
- Subgroups at high risk for ischaemic heart disease:identification and validation in 67 000 individuals from the general population.International journal of epidemiology · 2015Article
- Validated context-dependent associations of coronary heart disease risk with genotype variation in the chromosome 9p21 region: the Atherosclerosis Risk in Communities study.Human genetics · 2014Article
- Efficient identification of context dependent subgroups of risk from genome-wide association studies.Statistical applications in genetics and molecular biology · 2014Article
- Common clinical practice versus new PRIM score in predicting coronary heart disease risk.Atherosclerosis · 2010Article
- Context-dependent associations between variation in risk of ischemic heart disease and variation in the 5' promoter region of the apolipoprotein E gene in Danish women.Circulation. Cardiovascular genetics · 2010Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
This article extends the Patient Rule-Induction Method (PRIM) for modeling cumulative incidence of disease developed by Dyson et al. (Genet Epidemiol 31:515-527) to include the simultaneous consideration of non-additive combinations of predictor variables, a significance test of each combination, an adjustment for multiple testing and a confidence interval for the estimate of the cumulative incidence of disease in each partition. We employ the partitioning algorithm component of the Combinatorial Partitioning Method to construct combinations of predictors, permutation testing to assess the significance of each combination, theoretical arguments for incorporating a multiple testing adjustment and bootstrap resampling to produce the confidence intervals. An illustration of this revised PRIM utilizing a sample of 2,258 European male participants from the Copenhagen City Heart Study is presented that assesses the utility of genetic variants in predicting the presence of ischemic heart disease beyond the established risk factors.
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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.