How Can the Similarity of the Case and Control Groups be Measured in Case-Control Studies?

Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

doi:10.1109/iwobi47054.2019.9114390

Cited by 2 publications

(4 citation statements)

References 24 publications

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“…The main drawback of these tests is that they evaluate the similarity of the treated group and the control group on only a single covariate. Calculating the Distribution Dissimilarity Index (DDI) [31] opens another aspect to the evaluations, as it calculates the similarity of the covariates based on the differences in the frequencies of the histograms of the covariates. In biomedical studies, the Hansen-Bowers test [32] is applied for more complex evaluations.…”

Section: Related Workmentioning

confidence: 99%

“…As we mentioned before, the similarity of the distributions of the covariates does not mean that the matched individuals are also similar pairwise. The pairwise similarity of the matched objects can be evaluated by the Nearest Neighbour Index (NNI) and the Global Dissimilarity Index (GDI) [31]. The first measure only evaluates whether the paired individual is the closest individual from the candidate elements, while the second one considers the degree of the difference.…”

Section: Related Workmentioning

confidence: 99%

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Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection

Szekér

Vathy-Fogarassy

2021

Algorithms

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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 n-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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection

Szekér

Vathy-Fogarassy

2021

Algorithms

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“…The application limit of the test, namely that it can be applied for 1:1 matching without replacement, made it ideal for our study. To extend the complex evaluation for all covariate distributions together, the Distribution Dissimilarity Index (DDI) presented in [57] was also utilized. This measure first compares the disparity between histograms for each covariate separately and calculates the differences of the frequencies of the histogram bins before the differences are totalled and normalized.…”

Section: Plos Onementioning

confidence: 99%

“…As the similarity of the distributions of the covariates does not mean that the paired individuals are also similar, two aggregated pairwise similarity measures, the Nearest Neighbour Index (NNI) and the Global Dissimilarity Index (GDI) [57], were also calculated for each comparison. The common characteristic of these measures is that the overall distance of the case and control groups is calculated by evaluating the pairwise distances of the matched subjects.…”

Section: Plos Onementioning

confidence: 99%

Weighted nearest neighbours-based control group selection method for observational studies

Szekér

Vathy-Fogarassy

2020

PLoS ONE

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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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How Can the Similarity of the Case and Control Groups be Measured in Case-Control Studies?

Cited by 2 publications

References 24 publications

Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection

Optimized Weighted Nearest Neighbours Matching Algorithm for Control Group Selection

Weighted nearest neighbours-based control group selection method for observational studies

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