ArticleScientific reports2016
A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Medication adherence prediction through temporal modelling in cardiovascular disease management.BMC medical informatics and decision making · 2022Article
- Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database.BMC medical informatics and decision making · 2022Article
- Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network.Frontiers in public health · 2022Article
- A framework for the risk prediction of avian influenza occurrence: An Indonesian case study.PloS one · 2021Article
- Understanding the hidden relations between pro- and anti-inflammatory cytokine genes in bovine oviduct epithelium using a multilayer response surface method.Scientific reports · 2019Article
- The Environmental Determinants of Diabetes in the Young (TEDDY) Study: 2018 Update.Current diabetes reports · 2018Review
- Data-based Decision Rules to Personalize Depression Follow-up.Scientific reports · 2018Article
- An autoregressive logistic model to predict the reciprocal effects of oviductal fluid components on in vitro spermophagy by neutrophils in cattle.Scientific reports · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.
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Registered trials
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