This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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533
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section for Statistical Programmers and Analysts
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Abstract - #309114 |
Title:
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SAS Macro for Multiple Simple Logistic Regression Analyses with Application to Predictors of New Onset Diabetes After Renal Transplant (NODAT)
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Author(s):
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Brie N. Noble*+ and Amylou C. Dueck and Joseph G. Hentz and Qing Wu and Harini A. Chakkera
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Companies:
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Mayo Clinic and Mayo Clinic and Mayo Clinic and Mayo Clinic and Mayo Clinic
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Address:
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13400 E. Shea Blvd, Scottsdale, AZ, 85259,
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Keywords:
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SAS ;
macro ;
renal transplant ;
logistic regression ;
new onset diabetes
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Abstract:
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New onset diabetes after renal transplant (NODAT) has adverse effects on both patient and graft survival. A first step to identifying pre-transplant measures which predict NODAT is simple logistic regression analyses using multiple predictor variables. We developed a SAS macro to summarize multiple simple logistic regression analyses in a concise table including odds ratios, confidence intervals, and p-values. Special features include options for AMA standardized p-values and unit specification for meaningful odds ratio interpretation of continuous predictors. Macro features are demonstrated via application to a cohort of 377 patients undergoing renal transplant. We identified that pre-transplant age, BMI, fasting glucose, steroid use, and the use of gout medication were statistically significant (P< 0.05) simple predictors of NODAT 1 year post renal transplant. SAS code will be provided.
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