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10.1080/09286586.2016.1259636 http://scihub22266oqcxt.onion/10.1080/09286586.2016.1259636 C5597052!5597052!28102741     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28102741&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Ophthalmic+Epidemiol 2017 ; 24 (2): 130-40 Nephropedia Template TP {{tp|p=28102741|t=2017. Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data |pdf=|usr=}}{{28102741}} gab.com Text Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data JO: Ophthalmic Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=28102741 #FOAvip Purpose: To describe and demonstrate appropriate linear regression methods for analyzing correlated continuous eye data. Methods: We describe several approaches to regression analysis involving both eyes, including mixed effects and marginal models under various covariance structures to account for inter-eye correlation. We demonstrate, with SAS statistical software, applications in a study comparing baseline refractive error between one eye with choroidal neovascularization (CNV) and the unaffected fellow eye, and in a study determining factors associated with visual field data in the elderly. Results: When refractive error from both eyes were analyzed with standard linear regression without accounting for inter-eye correlation (adjusting for demographic and ocular covariates), the difference between eyes with CNV and fellow eyes was 0.15 diopters (D; 95% confidence interval, CI ?0.03 to 0.32D, P=0.10). Using a mixed effects model or a marginal model, the estimated difference was the same but with narrower 95% CI (0.01 to 0.28D, P=0.03). Standard regression for visual field data from both eyes provided biased estimates of standard error (generally underestimated) and smaller P-values, while analysis of the worse eye provided larger P-values than mixed effects models and marginal models. Conclusion: In research involving both eyes, ignoring inter-eye correlation can lead to invalid inferences. Analysis using only right or left eyes is valid, but decreases power. Worse-eye analysis can provide less power and biased estimates of effect. Mixed effects or marginal models using the eye as the unit of analysis should be used to appropriately account for inter-eye correlation and maximize power and precision. Twit Text FOAVip Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data ????Ophthalmic Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=28102741 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28102741 #FOAvip English Wikipedia <ref>{{Cite pmid|28102741}}</ref> Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data #MMPMID28102741 Ying Gs; Maguire MG; Glynn R; Rosner B Ophthalmic Epidemiol 2017[Apr]; 24 (2): 130-40 PMID28102741show ga Purpose: To describe and demonstrate appropriate linear regression methods for analyzing correlated continuous eye data. Methods: We describe several approaches to regression analysis involving both eyes, including mixed effects and marginal models under various covariance structures to account for inter-eye correlation. We demonstrate, with SAS statistical software, applications in a study comparing baseline refractive error between one eye with choroidal neovascularization (CNV) and the unaffected fellow eye, and in a study determining factors associated with visual field data in the elderly. Results: When refractive error from both eyes were analyzed with standard linear regression without accounting for inter-eye correlation (adjusting for demographic and ocular covariates), the difference between eyes with CNV and fellow eyes was 0.15 diopters (D; 95% confidence interval, CI ?0.03 to 0.32D, P=0.10). Using a mixed effects model or a marginal model, the estimated difference was the same but with narrower 95% CI (0.01 to 0.28D, P=0.03). Standard regression for visual field data from both eyes provided biased estimates of standard error (generally underestimated) and smaller P-values, while analysis of the worse eye provided larger P-values than mixed effects models and marginal models. Conclusion: In research involving both eyes, ignoring inter-eye correlation can lead to invalid inferences. Analysis using only right or left eyes is valid, but decreases power. Worse-eye analysis can provide less power and biased estimates of effect. Mixed effects or marginal models using the eye as the unit of analysis should be used to appropriately account for inter-eye correlation and maximize power and precision. äTutorial on Biostatistics: Linear Regression Analysis of Continuous Co(epmc).pdf DeepDyve Pubget Overpricing