What we need to know when calculating the coefficient of correlation?

Udovičić, Martina; Baždarić, Ksenija; Bilić-Zulle, Lidija; Petrovečki, Mladen

doi:10.11613/bm.2007.002

Cited by 101 publications

(74 citation statements)

References 1 publication

(2 reference statements)

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“…The correlation between two set of variables shows the extent to which changes in the value of one variable are correlated to the changes in the value of the other [Udovičić et al, 2007]. A larger positive correlation demonstrates a higher linear dependency between the cCCA model performance on real fMRI data and our simulation.…”

Section: Theorymentioning

confidence: 99%

A family of locally constrained CCA models for detecting activation patterns in fMRI

et al. 2017

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Canonical correlation analysis (CCA) has been used in functional Magnetic Resonance Imaging (fMRI) for improved detection of activation by incorporating time series from multiple voxels in a local neighborhood. To improve the specificity of local CCA methods, spatial constraints were previously proposed. In this study, constraints are generalized by introducing a family model of spatial constraints for CCA to further increase both sensitivity and specificity in fMRI activation detection. The proposed locally-constrained CCA (cCCA) model is formulated in terms of a multivariate constrained optimization problem and solved efficiently with numerical optimization techniques. To evaluate the performance of this cCCA model, simulated data are generated with a Signal-To-Noise Ratio of 0.25, which is realistic to the noise level contained in episodic memory fMRI data. Receiver operating characteristic (ROC) methods are used to compare the performance of different models. The cCCA model with optimum parameters (called optimum-cCCA) obtains the largest area under the ROC curve. Furthermore, a novel validation method is proposed to validate the selected optimum-cCCA parameters based on ROC from simulated data and real fMRI data. Results for optimum-cCCA are then compared with conventional fMRI analysis methods using data from an episodic memory task. Wavelet-resampled resting-state data are used to obtain the null distribution of activation. For simulated data, accuracy in detecting activation increases for the optimum-cCCA model by about 43% as compared to the single voxel analysis with comparable Gaussian smoothing. Results from the real fMRI data set indicate a significant increase in activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.

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Section: Theorymentioning

confidence: 99%

A family of locally constrained CCA models for detecting activation patterns in fMRI

et al. 2017

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“…The use and interpretation of correlation analysis is nicely reviewed by Udovicic M et al . in Biochemia Medica (13). …”

Section: Correlation Analysismentioning

confidence: 99%

Practical recommendations for statistical analysis and data presentation in Biochemia Medica journal

Šimundić¹

2012

Biochem Med

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The aim of this article is to highlight practical recommendations based on our experience as reviewers and journal editors and refer to some most common mistakes in manuscripts submitted to Biochemia Medica. One of the most important parts of the article is the Abstract. Authors quite often forget that Abstract is sometimes the fi rst (and only) part of the article read by the readers. The article Abstract must therefore be comprehensive and provide key results of your work. Problematic part of the article, also often neglected by authors is the subheading Statistical analysis, within Materials and methods, where authors must explain which statistical tests were used in their data analysis and the rationale for using those tests. They also need to make sure that all tests used are listed under Statistical analysis section, as well as that all tests listed are indeed used in the study. When writing Results section there are several key points to keep in mind, such as: are results presented with adequate precision and accurately; is descriptive analysis appropriate; is the measure of confi dence provided for all estimates; if necessary and applicable, are correct statistical tests used for analysis; is P value provided for all tests, etc. Especially important is not to make any conclusions on the causal relationship unless the study is an experiment or clinical trial. We believe that the use of the proposed checklist might increase the quality of the submitted work and speed up the peer-review and publication process for published articles.

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“…Ultrasound size and pathological size showed the best correlation (r=0.845). This would correspond with very good correlation [19]. The correlation coefficients for clinical measurement and mammogram were 0.717 and 0.713.…”

Section: Size Comparisonsmentioning

confidence: 58%

“…The correlation coefficients for clinical measurement and mammogram were 0.717 and 0.713. These correlations can be categorized as moderate to good [19]. From the lines representing 95% confidence intervals of the means, the size values of all modalities deviated from the line of true fit as lesions increase in size.…”

Section: Size Comparisonsmentioning

confidence: 96%

Multimodal assessment of mammogram, ultrasound and clinical palpation in relation to pathological size of breast carcinoma

Fadzli¹,

Rahmat²,

MN³

et al. 2018

Integr Cancer Sci Therap

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Objectives: Ultrasonography, mammography and clinical examination (conventional triple assessment modalities) are compared to the gold standard of pathological size to assess their accuracy in measuring size of palpable breast cancer lesions. This knowledge has an important role in the patient's further management: for staging, choice of surgical technique and prognostication.Patients and methods: 71 tumours were studied in 70 patients who were prospectively collected over a 2 year period. Pearson's correlation test and Bland-Altman's plot were used to analyse tumour size by clinical palpation, ultrasound and mammogram.Results: Histopathological examination revealed 60 invasive carcinomas (58 ductal type, 2 lobular type), 4 ductal carcinoma in situ, 2 mixed and 5 special types. The pathological size varied from 0.5cm to 9.5cm. Ultrasound had the best correlation with pathological size, with r= 0.845 (p< 0.001), but with a tendency to underestimate. Size correlation for mammogram and clinical palpation were similar and statistically significant. However, the standard deviation of mammographic size was more compared to size on clinical examination (1.3 (mammogram) vs. 2.2 (clinical)). Clinical palpation was inclined to overestimate, whereas mammogram neither over-nor under-estimated size. Measurements using ultrasonography produced the lowest standard deviation, thus a lower variability from the mean. Conclusion:This study demonstrated that ultrasound is the more accurate modality compared to mammogram and clinical palpation size for the measurement of palpable breast cancer lesions. However, ultrasound may downstage tumours due to its tendency to underestimate size.

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What we need to know when calculating the coefficient of correlation?

Cited by 101 publications

References 1 publication

A family of locally constrained CCA models for detecting activation patterns in fMRI

A family of locally constrained CCA models for detecting activation patterns in fMRI

Practical recommendations for statistical analysis and data presentation in Biochemia Medica journal

Multimodal assessment of mammogram, ultrasound and clinical palpation in relation to pathological size of breast carcinoma

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