ArticleStatistical applications in genetics and molecular biology2014
Efficient identification of context dependent subgroups of risk from genome-wide association studies.
Article in Statistical applications in genetics and molecular biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 5 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
- The extrema of circulating miR-17 are identified as biomarkers for aggressive prostate cancer.American journal of cancer research · 2018Article
- Subgroups at high risk for ischaemic heart disease:identification and validation in 67 000 individuals from the general population.International journal of epidemiology · 2015Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
We have developed a modified Patient Rule-Induction Method (PRIM) as an alternative strategy for analyzing representative samples of non-experimental human data to estimate and test the role of genomic variations as predictors of disease risk in etiologically heterogeneous sub-samples. A computational limit of the proposed strategy is encountered when the number of genomic variations (predictor variables) under study is large (>500) because permutations are used to generate a null distribution to test the significance of a term (defined by values of particular variables) that characterizes a sub-sample of individuals through the peeling and pasting processes. As an alternative, in this paper we introduce a theoretical strategy that facilitates the quick calculation of Type I and Type II errors in the evaluation of terms in the peeling and pasting processes carried out in the execution of a PRIM analysis that are under-estimated and non-existent, respectively, when a permutation-based hypothesis test is employed. The resultant savings in computational time makes possible the consideration of larger numbers of genomic variations (an example genome-wide association study is given) in the selection of statistically significant terms in the formulation of PRIM prediction models.
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Registered trials
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