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Testing conditional multivariate rank correlations: the effect of institutional quality on factors influencing competitiveness.

ABSTRACT: Joint distribution between two or more variables could be influenced by the outcome of a conditioning variable. In this paper, we propose a flexible Wald-type statistic to test for such influence. The test is based on a conditioned multivariate Kendall's tau nonparametric estimator. The asymptotic properties of the test statistic are established under different null hypotheses to be tested for, such as conditional independence or testing for constant conditional dependence. Two simulation studies are presented: The first shows that the estimator proposed and the bandwidth selection procedure perform well. The second presents different bivariate and multivariate models to check the size and power of the test and runs comparisons with previous proposals when appropriate. The results support the contention that the test is accurate even in complex situations and that its computational cost is low. As an empirical application, we study the dependence between some pillars of European Regional Competitiveness when conditioned on the quality of regional institutions. We find interesting results, such as weaker links between innovation and higher education in regions with lower institutional quality.

Supplementary information

The online version contains supplementary material available at 10.1007/s11749-022-00806-1.

SUBMITTER: Ascorbebeitia J

PROVIDER: S-EPMC8972772 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Testing conditional multivariate rank correlations: the effect of institutional quality on factors influencing competitiveness.

Ascorbebeitia Jone J Ferreira Eva E Orbe Susan S

Test (Madrid, Spain) 20220401 4

Joint distribution between two or more variables could be influenced by the outcome of a conditioning variable. In this paper, we propose a flexible Wald-type statistic to test for such influence. The test is based on a conditioned multivariate Kendall's tau nonparametric estimator. The asymptotic properties of the test statistic are established under different null hypotheses to be tested for, such as conditional independence or testing for constant conditional dependence. Two simulation studie ...[more]

PMID: 35382496

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Project description:PurposeThis study aimed to describe the demographic and clinical characteristics of cancer patients with COVID-19, exploring factors associated with adverse outcomes.Patients and methodsThis retrospective cohort study methodically extracted and curated data from electronic medical records (EMRs) of numerous healthcare institutions on cancer patients diagnosed with a confirmed SARS-CoV-2 infection between May 2020 and August 2021, to identify risk factors linked to extended hospitalization and mortality. The retrieved information encompassed the patients' demographic and clinical characteristics, including the incidence of prolonged hospitalization, acute complications, and COVID-19-related mortality.ResultsA total of 1446 cancer patients with COVID-19 were identified (mean [Standard deviation] age, 59.2 [14.3] years). Most patients were female (913 [63.1%]), non-white (646 [44.7%]), with non-metastatic (818 [56.6%]) solid tumors (1318 [91.1%]), and undergoing chemotherapy (647 [44.7%]). The rate of extended hospitalization due to COVID-19 was 46% (n = 665), which was significantly impacted by age (p = 0.012), sex (p = 0.003), race and ethnicity (p = 0.049), the presence of two or more comorbidities (p = 0.006), hematologic malignancies (p = 0.013), metastatic disease (p = 0.002), and a performance status ≥ 2 (p = 0.001). The COVID-19-related mortality rate was 18.9% (n = 273), and metastatic disease (<0.001), performance status ≥2 (<0.001), extended hospitalization (p = 0.028), renal failure (p = 0.029), respiratory failure (p < 0.001), sepsis (p = 0.004), and shock (p = 0.040) significantly and negatively influenced survival.ConclusionThe rate of extended hospitalization and COVID-19-specific death in cancer patients was notably high and could be influenced by comorbidities, cancer treatment status, and clinical fragility. These observations may aid in developing risk counseling strategies regarding COVID-19 in individuals diagnosed with cancer.

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Testing conditional multivariate rank correlations: the effect of institutional quality on factors influencing competitiveness.

Supplementary information

Publications

Testing conditional multivariate rank correlations: the effect of institutional quality on factors influencing competitiveness.

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