PubMed · 6793135
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S M Gore. 1981-09-12. Assessing methods- recognising linearity.. https://doi.org/10.1136/bmj.283.6293.711
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MOTIVATION: Compositional data are frequently encountered in many disciplines, such as in next-generation sequencing experiments widely used in biomedical studies. Regression analysis with compositional data as either responses or predictors has been well studied. However, when both responses and predictors are compositional, the inventory of analysis tools is surprisingly limited, especially in the high-dimensional setting. Among the few existing methods, most of them rely on a log-ratio transformation to move compositional data from the simplex to real numbers. Yet, a serious weakness of these methods is their failure to handle the substantial fraction of zeroes observed in data collected from next-generation sequencing experiments. RESULTS: To investigate associations between two high-dimensional multi-omics compositions, we propose a composition-on-composition (COC) regression analysis method which does not require log-ratio transformations and hence can handle zeroes in the data. To account for high dimensionality, we estimate regression coefficients using a penalized estimation equation approach. Finally, inference procedures for COC regression are also proposed. Superior performance of COC is demonstrated through both comprehensive numerical simulations and case studies. AVAILABILITY AND IMPLEMENTATION: Source R codes to implement COC method is available at https://github.com/nrios4/COC.
Bland-Altman analysis for comparison of two methods of clinical measurement is frequently used in scientific publications. This article is more appropriate than the conventional linear regression analysis. This paper gives an overview of the principles for the use of Bland-Altman analysis as well as the specific terminology attached to it. The Bland-Altman comparison analysis is mainly a tool for clinical interpretation. The bias and the agreement limits provide the variation of the values of the technique compared to the other. The difference between the two methods of measurement is plotted against the average obtained with each of the two techniques. Bland-Altman analysis can also be used to check the repeatability of a measurement technique within the same subject and to determine a repeatability coefficient. With an adaptation of the calculation of the agreement limits, the average of multiple measurements for each subject with two measurement techniques can be used for the Bland-Altman analysis.
Power analysis guides researchers in planning how much data to collect. This article describes BW-Power, a computer program for the Windows 95 environment that performs power analyses for research designs that may or may not include both between- and within-subjects factors. We discuss how BWPower easily accommodates both between- and within-subjects factors and provide examples of BWPower's use in performing power analyses on designs with only between-subjects factors, designs with only repeated measures, and with mixed between- and within-subjects designs. We highlight the major features of BWPower's user interface, such as the ability to iteratively increment or decrement the number of subjects and the automatic recalculation of power when the number of subjects or effect sizes is changed.