A Euclidean Distance-Based Matching Procedure for Nonrandomized Comparison Studies

Spiel, Christiane; Lapka, Dominik; Gradinger, Petra; Zodlhofer, Eva Maria; Reimann, Ralph; Schober, Barbara; Wagner, Petra; Eye, Alexander von

doi:10.1027/1016-9040.13.3.180

Cited by 21 publications

(17 citation statements)

References 22 publications

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“…To understand how the duration and relapse rate compared to a similar population not suffering from COVID-19, we selected an additional matched sample from all app users who met the study inclusion criteria but who tested negative by PCR swab test, and, for each individual with COVID-19, we chose the individual from the negative group with the smallest Euclidean distance based on sex, age and BMI 13 .…”

Section: Methodsmentioning

confidence: 99%

Attributes and predictors of long COVID

Sudre

Murray

Varsavsky

et al. 2021

Nat Med

1,882

224

1,792

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Section: Methodsmentioning

confidence: 99%

Attributes and predictors of long COVID

Sudre

Murray

Varsavsky

et al. 2021

Nat Med

1,882

224

1,792

View full text Add to dashboard Cite

“…We repeated analyses in an independent subgroup of 2472 app users who reported positive testing for antibodies against SARS-CoV2 more than 2 weeks after symptom onset, but without swab test results ( Supplementary Figure 1). 16 .…”

Section: Datasetmentioning

confidence: 99%

“…To understand how the relapse rate compared to a comparable population not suffering from COVID-19, we selected an additional matched sample from all app users meeting study inclusion criteria but testing negative by PCR swab test, choosing for each COVID+ case the individual from the negative group with the smallest Euclidean distance based on sex, age and BMI 16 .…”

Section: Datasetmentioning

confidence: 99%

Attributes and predictors of Long-COVID: analysis of COVID cases and their symptoms collected by the Covid Symptoms Study App

Sudre

Murray

Varsavsky

et al. 2020

Preprint

238

246

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Reports of "Long-COVID", are rising but little is known about prevalence, risk factors, or whether it is possible to predict a protracted course early in the disease. We analysed data from 4182 incident cases of COVID-19 who logged their symptoms prospectively in the COVID Symptom Study app. 558 (13.3%) had symptoms lasting >28 days, 189 (4.5%) for >8 weeks and 95 (2.3%) for >12 weeks. Long-COVID was characterised by symptoms of fatigue, headache, dyspnoea and anosmia and was more likely with increasing age, BMI and female sex. Experiencing more than five symptoms during the first week of illness was associated with Long-COVID, OR=3.53 [2.76;4.50]. Our model to predict long-COVID at 7 days, which gained a ROC-AUC of 76%, was replicated in an independent sample of 2472 antibody positive individuals. This model could be used to identify individuals for clinical trials to reduce long-term symptoms and target education and rehabilitation services.

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“…First, there are multiple ways to match controls with cases. For example, Euclidian distance seems to be a promising way of matching cases and controls [30]. Second, PSM might be applied to the variable selection step by considering the pure additive contribution of genetic variants.…”

Section: Discussionmentioning

confidence: 99%

Pure additive contribution of genetic variants to a risk prediction model using propensity score matching: application to type 2 diabetes

2019

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The achievements of genome-wide association studies have suggested ways to predict diseases, such as type 2 diabetes (T2D), using single-nucleotide polymorphisms (SNPs). Most T2D risk prediction models have used SNPs in combination with demographic variables. However, it is difficult to evaluate the pure additive contribution of genetic variants to classically used demographic models. Since prediction models include some heritable traits, such as body mass index, the contribution of SNPs using unmatched case-control samples may be underestimated. In this article, we propose a method that uses propensity score matching to avoid underestimation by matching case and control samples, thereby determining the pure additive contribution of SNPs. To illustrate the proposed propensity score matching method, we used SNP data from the Korea Association Resources project and reported SNPs from the genome-wide association study catalog. We selected various SNP sets via stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and the elastic-net (EN) algorithm. Using these SNP sets, we made predictions using SLR, LASSO, and EN as logistic regression modeling techniques. The accuracy of the predictions was compared in terms of area under the receiver operating characteristic curve (AUC). The contribution of SNPs to T2D was evaluated by the difference in the AUC between models using only demographic variables and models that included the SNPs. The largest difference among our models showed that the AUC of the model using genetic variants with demographic variables could be 0.107 higher than that of the corresponding model using only demographic variables.

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A Euclidean Distance-Based Matching Procedure for Nonrandomized Comparison Studies

Cited by 21 publications

References 22 publications

Attributes and predictors of long COVID

Attributes and predictors of long COVID

Attributes and predictors of Long-COVID: analysis of COVID cases and their symptoms collected by the Covid Symptoms Study App

Pure additive contribution of genetic variants to a risk prediction model using propensity score matching: application to type 2 diabetes

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