Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection
An essential criterion for the proper implementation of case-control studies is selecting appropriate case and control groups. In this article, a new simulated annealing-based control group selection method is proposed, which solves the problem of selecting individuals in the control group as a dist...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/14/12/356 |
| _version_ | 1827674463345311744 |
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| author | Szabolcs Szekér Ágnes Vathy-Fogarassy |
| author_facet | Szabolcs Szekér Ágnes Vathy-Fogarassy |
| author_sort | Szabolcs Szekér |
| collection | DOAJ |
| description | An essential criterion for the proper implementation of case-control studies is selecting appropriate case and control groups. In this article, a new simulated annealing-based control group selection method is proposed, which solves the problem of selecting individuals in the control group as a distance optimization task. The proposed algorithm pairs the individuals in the <i>n</i>-dimensional feature space by minimizing the weighted distances between them. The weights of the dimensions are based on the odds ratios calculated from the logistic regression model fitted on the variables describing the probability of membership of the treated group. For finding the optimal pairing of the individuals, simulated annealing is utilized. The effectiveness of the newly proposed Weighted Nearest Neighbours Control Group Selection with Simulated Annealing (WNNSA) algorithm is presented by two Monte Carlo studies. Results show that the WNNSA method can outperform the widely applied greedy propensity score matching method in feature spaces where only a few covariates characterize individuals and the covariates can only take a few values. |
| first_indexed | 2024-03-10T04:40:42Z |
| format | Article |
| id | doaj.art-98be8316a4354d6abf6dfeabb7217385 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T04:40:42Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-98be8316a4354d6abf6dfeabb72173852023-11-23T03:24:54ZengMDPI AGAlgorithms1999-48932021-12-01141235610.3390/a14120356Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group SelectionSzabolcs Szekér0Ágnes Vathy-Fogarassy1Department of Computer Science and Systems Technology, Faculty of Information Technology, University of Pannonia, 8200 Veszprém, HungaryDepartment of Computer Science and Systems Technology, Faculty of Information Technology, University of Pannonia, 8200 Veszprém, HungaryAn essential criterion for the proper implementation of case-control studies is selecting appropriate case and control groups. In this article, a new simulated annealing-based control group selection method is proposed, which solves the problem of selecting individuals in the control group as a distance optimization task. The proposed algorithm pairs the individuals in the <i>n</i>-dimensional feature space by minimizing the weighted distances between them. The weights of the dimensions are based on the odds ratios calculated from the logistic regression model fitted on the variables describing the probability of membership of the treated group. For finding the optimal pairing of the individuals, simulated annealing is utilized. The effectiveness of the newly proposed Weighted Nearest Neighbours Control Group Selection with Simulated Annealing (WNNSA) algorithm is presented by two Monte Carlo studies. Results show that the WNNSA method can outperform the widely applied greedy propensity score matching method in feature spaces where only a few covariates characterize individuals and the covariates can only take a few values.https://www.mdpi.com/1999-4893/14/12/356control group selectionweighted k-nearest neighboursimulated annealinglogistic regressionnegative covariates |
| spellingShingle | Szabolcs Szekér Ágnes Vathy-Fogarassy Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection Algorithms control group selection weighted k-nearest neighbour simulated annealing logistic regression negative covariates |
| title | Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection |
| title_full | Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection |
| title_fullStr | Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection |
| title_full_unstemmed | Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection |
| title_short | Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection |
| title_sort | optimized weighted nearest neighbours matching algorithm for control group selection |
| topic | control group selection weighted k-nearest neighbour simulated annealing logistic regression negative covariates |
| url | https://www.mdpi.com/1999-4893/14/12/356 |
| work_keys_str_mv | AT szabolcsszeker optimizedweightednearestneighboursmatchingalgorithmforcontrolgroupselection AT agnesvathyfogarassy optimizedweightednearestneighboursmatchingalgorithmforcontrolgroupselection |