EventsMOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed.
Published
This submission belongs to the session 03. USEDAT-01: USA-Europe Data Analysis Training Congress, Cambridge, UK-Bilbao, Spain-Miami, USA, 2015 of the event MOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed.
Published date
07 Dec, 2015
Citation
Urko Aguirre, Inmaculada Arostegui, Jose M. Quintana, A Computer-Aided SAS Macro for the Evaluation of the Simulation Performances in Missingness Settings, in Proceedings of MOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed., 5 December–15 December 2015, MDPI: Basel, Switzerland, doi: 10.3390/MOL2NET-1-e012
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A Computer-Aided SAS Macro for the Evaluation of the Simulation Performances in Missingness Settings

Inmaculada Arostegui 2,3
Jose M. Quintana 1
1. Research Unit, REDISSEC: Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas, Hospital Galdakao-Usansolo, Galdakao, Spain;
2. Department of Applied Mathematics, Statistics and Operational Research; REDISSEC: Red de Investigación en Servicios Sanitarios y Enfermedades Crónicas. Faculty of Science and Technology, Leioa. Spain
3. BCAM-Basque Center for Applied Mathematics, Bilbao, Spain.
Abstract

Model validation has become a topic of great interest to many fields such as industry, medicine or even to government. Its main challenge is to provide stable and credible tools so that the decision-maker with the information necessary can make high-consequence judgments.  This process requires simulation modelling and consequently, some guidelines or evaluation criteria are essential in order to draw meaningful conclusions. A computer-aided SAS® macro is developed using the SAS/IML programming language.  Researchers should provide the dataset to be analyzed and the true values to be compared. As a result, the statistical program shows measures (i.e., number of simulations to be performed, bias, accuracy, coverage, etc…) which help investigators to make decisions with a minimal effort of programming.  Numerical results of the aforementioned statistical parameters, plots and a report are returned by the statistical tool. Although this macro is focused on the missingness setting, it is applicable to any other discipline. We encourage researchers to use it to make better statistical assessments of the used methods.

Keywords
simulation
missing data
bias
validation
SAS macro
Manuscript
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