ArticlePloS one2020
Weighted nearest neighbours-based control group selection method for observational studies.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Association between maternal SARS-CoV-2 infection & clinical outcomes in infants: A multicentric retrospective cohort study in India.The Indian journal of medical research · 2025Article
- Association between prescription drugs and all-cause mortality risk in the UK population.Aging cell · 2024Article
- Estimating the under-five malaria risk in Uganda based on the nearest neighbour matched analysis technique.African health sciences · 2024Article
- Unraveling stroke gait deviations with movement analytics, more than meets the eye: a case control study.Frontiers in neuroscience · 2024Article
- Global multi-center and multi-modal magnetic resonance imaging study of obsessive-compulsive disorder: Harmonization and monitoring of protocols in healthy volunteers and phantoms.International journal of methods in psychiatric research · 2023Article
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2 authors.
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Abstract
Although in observational studies, propensity score matching is the most widely used balancing method, it has received much criticism. The main drawback of this method is that the individuals of the case and control groups are paired in the compressed one-dimensional space of propensity scores. In this paper, such a novel multivariate weighted k-nearest neighbours-based control group selection method is proposed which can eliminate this disadvantage of propensity score matching. The proposed method pairs the elements of the case and control groups in the original vector space of the covariates and the dissimilarities of the individuals are calculated as the weighted distances of the subjects. The weight factors are calculated from a logistic regression model fitted on the status of treatment assignment. The efficiency of the proposed method was evaluated by Monte Carlo simulations on different datasets. Experimental results show that the proposed Weighted Nearest Neighbours Control Group Selection with Error Minimization method is able to select a more balanced control group than the most widely applied greedy form of the propensity score matching method, especially for individuals characterized with few descriptive features.
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