2019
DOI: 10.3389/fdata.2019.00004
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Matching Cases and Controls Using SAS® Software

Abstract: Matching is frequently used in observational studies, especially in medical research. However, only a small number of articles with matching programs for the SAS software (SAS Institute Inc., Cary, NC, USA) are available, even less are usable for inexperienced users of SAS software. This article presents a matching program for the SAS software and links to an online repository for examples and test data. The program enables matching on several variables and includes in-depth explanation of the expressions used… Show more

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Cited by 20 publications
(18 citation statements)
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“…These criteria identified 3,544 unmatched hospital-based controls with complete demographic, clinical, and genetic data. To minimize any confounding effects from predisposing risk factors, we also defined a matched dataset by matching hospital-based controls to cases at a ratio of 2:1 based on age, sex, obesity, hypertension, type 2 diabetes, and coronary artery disease (CAD) using a previously described algorithm ( 21 ). These criteria define a subset of 719 cases and 1,438 matched hospital-based controls with complete matching variables that were available for analysis.…”
Section: Methodsmentioning
confidence: 99%
“…These criteria identified 3,544 unmatched hospital-based controls with complete demographic, clinical, and genetic data. To minimize any confounding effects from predisposing risk factors, we also defined a matched dataset by matching hospital-based controls to cases at a ratio of 2:1 based on age, sex, obesity, hypertension, type 2 diabetes, and coronary artery disease (CAD) using a previously described algorithm ( 21 ). These criteria define a subset of 719 cases and 1,438 matched hospital-based controls with complete matching variables that were available for analysis.…”
Section: Methodsmentioning
confidence: 99%
“…For the primary analysis, confounding factors were identified using logistic regression with ECAD status as the outcome variable. Significant confounding variables (P < .05) were used to generate a matched population using a 2:1 matching procedure, as described by Mortensen et al 24 The secondary analysis was performed with univariate analysis in an unadjusted population. Statistical analyses were conducted using SAS Studio, version 3.8 (SAS Institute, Cary, NC).…”
Section: Discussionmentioning
confidence: 99%
“…Demographic characteristics (age at LND and sex) and site of LND were likewise extracted from histology reports. We used the macro for SAS software (SAS Institute Inc, Cary, North Carolina) developed by Mortensen et al 12 to match two randomly selected MKPs (controls) to each MUP patient (case) according to sex, 5‐year age group and LND site (two controls per case being the maximum number feasible across all study databases).…”
Section: Methodsmentioning
confidence: 99%