Improved procedures and computer programs for equivalence assessment of correlation coefficients.
Ontology highlight
ABSTRACT:
SUBMITTER: Shieh G
PROVIDER: S-EPMC8162672 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Coefficients for translational and rotational diffusion characterize the Brownian motion of particles. Emerging X-ray photon correlation spectroscopy (XPCS) experiments probe a broad range of length scales and time scales and are well-suited for investigation of Brownian motion. While methods for estimating the translational diffusion coefficients from XPCS are well-developed, there are no algorithms for measuring the rotational diffusion coefficients based on XPCS, even though the required raw data are accessible from such experiments. In this paper, we propose angular-temporal cross-correlation analysis of XPCS data and show that this information can be used to design a numerical algorithm (Multi-Tiered Estimation for Correlation Spectroscopy [MTECS]) for predicting the rotational diffusion coefficient utilizing the cross-correlation: This approach is applicable to other wavelengths beyond this regime. We verify the accuracy of this algorithmic approach across a range of simulated data.
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Project description:Spaced-learning refers to teaching spread over time, compared to mass-learning where the same duration of teaching is completed in one session. Our hypothesis is that spaced-learning is better than mass-learning in retaining microsurgical suturing skills. Medical students were randomized into mass-learning (single 8-h session) and spaced-learning (2-h weekly sessions over 4 weeks) groups. They were taught to place 9 sutures in a 4 mm-wide elastic strip. The primary outcome was precision of suture placement during a test conducted 1 month after completion of sessions. Secondary outcomes were time taken, cumulative performance, and participant satisfaction. 42 students (24 in the mass-learning group; 18 in spaced-learning group) participated. 3 students in the spaced-learning group were later excluded as they did not complete all sessions. Both groups had comparable baseline suturing skills but at 1 month after completion of teaching, the total score for suture placement were higher in spaced-learning group (27.63 vs 31.60,p = 0.04). There was no statistical difference for duration and satisfaction in either group. Both groups showed an improvement in technical performance over the sessions, but this did not differ between both groups. Microsurgical courses are often conducted in mass-learning format so spaced learning offers an alternative that enhances retention of complex surgical skills.
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Project description:BackgroundThe most common measure of association between two continuous variables is the Pearson correlation (Maronna et al. in Safari an OMC. Robust statistics, 2019. https://login.proxy.bib.uottawa.ca/login?url=https://learning.oreilly.com/library/view/-/9781119214687/?ar&orpq&email=^u). When outliers are present, Pearson does not accurately measure association and robust measures are needed. This article introduces three new robust measures of correlation: Taba (T), TabWil (TW), and TabWil rank (TWR). The correlation estimators T and TW measure a linear association between two continuous or ordinal variables; whereas TWR measures a monotonic association. The robustness of these proposed measures in comparison with Pearson (P), Spearman (S), Quadrant (Q), Median (M), and Minimum Covariance Determinant (MCD) are examined through simulation. Taba distance is used to analyze genes, and statistical tests were used to identify those genes most significantly associated with Williams Syndrome (WS).ResultsBased on the root mean square error (RMSE) and bias, the three proposed correlation measures are highly competitive when compared to classical measures such as P and S as well as robust measures such as Q, M, and MCD. Our findings indicate TBL2 was the most significant gene among patients diagnosed with WS and had the most significant reduction in gene expression level when compared with control (P value = 6.37E-05).ConclusionsOverall, when the distribution is bivariate Log-Normal or bivariate Weibull, TWR performs best in terms of bias and T performs best with respect to RMSE. Under the Normal distribution, MCD performs well with respect to bias and RMSE; but TW, TWR, T, S, and P correlations were in close proximity. The identification of TBL2 may serve as a diagnostic tool for WS patients. A Taba R package has been developed and is available for use to perform all necessary computations for the proposed methods.
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