Association, Causation, and Correlation

Truesdell, Alexander G.; Jayasuriya, Sasanka; Vallabhajosyula, Saraschandra

doi:10.1016/j.carrev.2021.06.128

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Therefore, exclusion of those with missing data would not have biased the results. Lastly, Because we evaluated statistical associations, the results do not imply causation [41].…”

Section: Plos Onementioning

confidence: 99%

Associations of symptom combinations with in-hospital mortality of coronavirus disease-2019 patients using South Korean National data

Nam

Kang

et al. 2022

PLoS ONE

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Background There are various risk factors for death in coronavirus disease-2019 (COVID-19) patients. The effects of symptoms on death have been investigated, but symptoms were considered individually, rather than in combination, as predictors. We examined the effects of symptom combinations on in-hospital mortality. Methods Data from the Korea Disease Control and Prevention Agency were analyzed. A cohort of 5,153 patients confirmed with COVID-19 in South Korea was followed from hospitalization to death or discharge. An exploratory factor analysis was performed to identify symptom combinations, and the hazard ratios (HRs) of death were estimated using the Cox proportional hazard model. Results Three sets of symptom factors were isolated for symptom combination. Factor 1 symptoms were cold-like symptoms, factor 2 were neurological and gastrointestinal symptoms, and factor 3 were more severe symptoms such as dyspnea and altered state of consciousness. Factor 1 (HR 1.14, 95% confidence interval [95% CI] 1.01–1.30) and factor 3 (HR 1.25, 95% CI 1.19–1.31) were associated with a higher risk for death, and factor 2 with a lower risk (HR 0.71, 95% CI 0.71–0.96). Conclusions The effect on in-hospital mortality differed according to symptom combination. The results are evidence of the effects of symptoms on COVID-19 mortality and may contribute to lowering the COVID-19 mortality rate. Further study is needed to identify the biological mechanisms underlying the effects of symptom combinations on mortality.

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“…Therefore, exclusion of those with missing data would not have biased the results. Lastly, Because we evaluated statistical associations, the results do not imply causation [41].…”

Section: Plos Onementioning

confidence: 99%

Associations of symptom combinations with in-hospital mortality of coronavirus disease-2019 patients using South Korean National data

Nam

Kang

et al. 2022

PLoS ONE

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“…Causal relationships are often manifested as a chain of cause and effect, where the exposure is the cause of the outcome (13). To establish a causal relationship between two variables, there are key steps that must be followed (14). First, identify the cause-and-effect variables.…”

Section: Introductionmentioning

confidence: 99%

Causal Roles of Serum Uric Acid Levels and Gout in Sepsis: A Mendelian Randomization Study

Qin,

Yang,

Ning

2024

Shock

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Objective Several epidemiological studies have identified a potential link between serum uric acid(UA), gout and sepsis. The primary objective of this study is to delve deeper into this connection, investigating the causal effect of UA and gout on sepsis by applying Mendelian Randomization (MR). Methods The causal relationship was analyzed using data from genome-wide association study(GWAS). Inverse variance weighting (IVW) was used as the main analysis method. Three complementary methods were used for our MR analysis, which included the MR-Egger regression method, the weighted median method, the simple median method. Horizontal pleiotropy was identified by MR-Egger intercept test. Cochran’s Q statistics were employed to assess the existence of instrument heterogeneity. The leave-one-out method was used as a sensitivity analysis. Results The IVW results indicated that there was a positive causal relationship between UA and sepsis(critical care) (OR = 0.24, 95%CI: 0.04 to 0.43, P = 0.018, F = 4291.20). There was no significant association between UA and sepsis(28 day death in critical care) (OR = 0.10, 95%CI: -0.29 to 0.50, P = 0.604). There was no significant association between gout and sepsis(critical care) (OR = 0.85, 95%CI: -4.87 to 6.57, P = 0.771), and sepsis(28 day death in critical care) (OR = -6.30, 95%CI: -17.41 to 4.81, P = 0.267). Horizontal pleiotropy was absent in this study. The results were robust under all sensitivity analyses. Conclusion The study revealed that elevated UA levels was causally linked with sepsis(critical care). No causal relationship had been found between UA and sepsis(28 day death in critical care), as well as between gout and sepsis.

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Critical physics-informed fatigue life prediction of laser 3D printed AlSi10Mg alloys with mass internal defects

Hu,

She,

et al. 2024

International Journal of Mechanical Sciences

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Association, Causation, and Correlation

Cited by 3 publications

References 9 publications

Associations of symptom combinations with in-hospital mortality of coronavirus disease-2019 patients using South Korean National data

Associations of symptom combinations with in-hospital mortality of coronavirus disease-2019 patients using South Korean National data

Causal Roles of Serum Uric Acid Levels and Gout in Sepsis: A Mendelian Randomization Study

Critical physics-informed fatigue life prediction of laser 3D printed AlSi10Mg alloys with mass internal defects

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