An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the ...relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians.
Handbook of Regression Analysis Chatterjee, Samprit; Simonoff, Jeffrey S
2013, 2013., 2012, 2013-01-03, 2013-05-30, Letnik:
5
eBook
A Comprehensive Account for Data Analysts of the Methods and Applications of Regression Analysis.Written by two established experts in the field, the purpose of the Handbook of Regression Analysis is ...to provide a practical, one-stop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of regression methods, but it has been deliberately written at an accessible level.The handbook provides a quick and convenient reference or “refresher” on ideas and methods that are useful for the effective analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (including linear, binary logistic, multinomial logistic, count, and nonlinear regression models). Theory underlying the methodology is presented when it advances conceptual understanding and is always supplemented by hands-on examples.References are supplied for readers wanting more detailed material on the topics discussed in the book. R code and data for all of the analyses described in the book are available via an author-maintained website.
...we note that use of 2-OCA is effective to prevent fluid leaks following therapeutic paracentesis (Table 1).Table 1. Paracentesis Fluid Leak Data Analysis No glue (NG) Glue (GG) Total leaks 25 ...(22.32%) 6 (5%) OR 5.45, P< 0.0001, 95% CI = 2.15, 13.89. chi square, logistic regression Large leaks 12 (10.7%) 1 (0.83%) OR 14.28, P 0.001, OR=14.28; 95% CI = 1.83, 111.73 Fisher’s exact test Logistic regression Small leak 13 (11.61%) 5 (4.16%) OR 3.02, P 0.047, 95% CI = 1.04; 8.76 Fisher's exact test No leaks 87 (77.6%) 114 (95%) N 112 120 Total=232 NG: Odds ratio, CI: confidence interval.
On univariable analysis, increasing stone size and number, impaction and sub-optimal contrast and scope image quality were significantly associated with decreased SF. ...sub-optimal contrast and ...scope image quality, increasing stone size and number, as well as impaction were significantly associated with decreased FE, in addition to smoking. Clinically relevant factors associated with stone fragmentation and final extraction Variables Stone fragmentation Final extraction Univariable analysis Multivariable analysis Univariable analysis Multivariable analysis OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) P-value Gender (reference female) 1.44(0.92-2.25) 0.49 N/A N/A 1.16 (0.76-1.75) 0.49 N/A N/A Smoking status (reference smoker) 1.52 (0.95-2.43) 0.08 N/A N/A 1.74 (1.13-2.67) 0.012 2.37 (1.18-4.76) 0.015 Number of stones: 2 or more (reference is 1 stone) 0.46(0.17-1.22) 0.19 N/A N/A 0.39 (0.16-0.92) 0.033 0.66 (0.06-6.98) 0.73 Largest stone size (in mm) 0.88 (0.84-0.92) < 0.001 0.86 (0.80-0.91) < 0.001 0.89 (0.85-0.93) < 0.001 0.83 (0.83-0.93) < 0.001 Intra-hepatic stone (reference is no) 1.21 (0.66-2.21) 0.54 N/A N/A 0.63 (0.37-1.09) 0.10 N/A N/A Impacted stone (reference is no) 0.33 (0.19-0.55) < 0.001 0.37 (0.16-0.87) 0.022 0.51 (0.30-0.85) 0.010 0.96 90.41-2.26) 0.93 Cholangioscope image quality:
Objectives: Describe Mexican patients with T1DM and find out if blood glucose (BG) testing frequency impacts glycemic targets (mean BG & estimated HbA1c eHbA1c).
Methods: Included mySugr users with a ...self-reported diagnosis of T1DM and had at least 2 BG logs in at least 14 days (G2D14) in the month prior to their first log entry. G2D14 is the lowest adherence needed to calculate eHbA1c. Users were stratified as low (G2D14 + G3D14) and high testing (G4D14 + G5D14). Subgroup analysis according to baseline eHbA1c was performed. Using a logistic regression model, an exploratory analysis to identify factors associated with BG decrease was also considered.
Results: 118,210 users (13% T1DM) were considered. Users in the highest testing subgroup (G5D14; n=276) in the first month before mySugr usage had a significantly lower baseline eHbA1c (-0.8 %; p < 0.01) compared with users in the lowest category (G2D14; n=254). For all users in the group G2D14 or above, eHbA1c decreased from baseline (1 mo. prior to mySugr usage) and stayed below baseline levels for the entire period of 5 mo. (n=253; mean differences: -0.3% at the first month of mySugr usage, -0.2% at the fourth month of mySugr usage; both changes significantly different from 0 with p < 0.05). No statistically significant difference in eHbA1c between the logging subgroups after 4 months of mySugr use was found. Nevertheless, high logging users with baseline eHbA1c > 8% had a non-significant higher reduction. In the logistic regression model, the factor with the strongest association with BG decrease was the baseline mean BG (log OR: 2.4).
Conclusion: in our RwD setting, the use of mySugr was associated with reduced eHbA1c. Baseline BG was statistically associated with BG reduction intensity. Although no significant difference in eHbA1c improvement between the high and low logging groups was found during mySugr usage, the baseline analysis suggests that users with increased logging had significantly better glycemic control before starting mySugr.
Disclosure
C. Vulcano: Employee; Roche Diabetes Care. H. Mikulski: Employee; Roche Diabetes Care. M. Mitter: Employee; Roche Diabetes Care. B. Ruch: Employee; Roche Diabetes Care.
