Generation of data with specific marginal risk difference

Background & Aim: Simulation studies are important statistical tools to investigate the performance of statistical models in specific situations. For a binary outcome and exposure, one of the most important statistical measures will be the risk difference (RD). To assess the quality of estimato...

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Main Authors: Kazem Mohammad, Mohammad Ali Mansournia, Safoora Gharibzadeh
Format: Article
Language:English
Published: Tehran University of Medical Sciences 2018-07-01
Series:Journal of Biostatistics and Epidemiology
Subjects:
Online Access:https://jbe.tums.ac.ir/index.php/jbe/article/view/154
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spelling doaj-5b703965c4e647298e1b1a572e48dfc32020-12-06T04:15:07ZengTehran University of Medical SciencesJournal of Biostatistics and Epidemiology2383-41962383-420X2018-07-0133/4Generation of data with specific marginal risk differenceKazem Mohammad0Mohammad Ali Mansournia1Safoora Gharibzadeh2Professor, Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, IranAssistant Professor, Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, IranDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Background & Aim: Simulation studies are important statistical tools to investigate the performance of statistical models in specific situations. For a binary outcome and exposure, one of the most important statistical measures will be the risk difference (RD). To assess the quality of estimators in estimating the effect of the exposure, a data set with a specific effect measure is require. Methods & Materials: Monte Carlo simulation can be helpful in situations when there is a proper data  generating  process.  In  this  paper,  another  technique  will  be  presented  to  generate  data  with specific marginal risk difference (MRD).  Results: Convergence of simulation methods in the same scenario reached in a few iterations using the proposed method.  Conclusion:  The  proposed  method  is  recommended  over  the  current  method  due  to  less  time consumption; this issue is important in studies with different scenarios.  https://jbe.tums.ac.ir/index.php/jbe/article/view/154Data systemsRisk ratioCausalityComputer simulationMonte Carlo method
collection DOAJ
language English
format Article
sources DOAJ
author Kazem Mohammad
Mohammad Ali Mansournia
Safoora Gharibzadeh
spellingShingle Kazem Mohammad
Mohammad Ali Mansournia
Safoora Gharibzadeh
Generation of data with specific marginal risk difference
Journal of Biostatistics and Epidemiology
Data systems
Risk ratio
Causality
Computer simulation
Monte Carlo method
author_facet Kazem Mohammad
Mohammad Ali Mansournia
Safoora Gharibzadeh
author_sort Kazem Mohammad
title Generation of data with specific marginal risk difference
title_short Generation of data with specific marginal risk difference
title_full Generation of data with specific marginal risk difference
title_fullStr Generation of data with specific marginal risk difference
title_full_unstemmed Generation of data with specific marginal risk difference
title_sort generation of data with specific marginal risk difference
publisher Tehran University of Medical Sciences
series Journal of Biostatistics and Epidemiology
issn 2383-4196
2383-420X
publishDate 2018-07-01
description Background & Aim: Simulation studies are important statistical tools to investigate the performance of statistical models in specific situations. For a binary outcome and exposure, one of the most important statistical measures will be the risk difference (RD). To assess the quality of estimators in estimating the effect of the exposure, a data set with a specific effect measure is require. Methods & Materials: Monte Carlo simulation can be helpful in situations when there is a proper data  generating  process.  In  this  paper,  another  technique  will  be  presented  to  generate  data  with specific marginal risk difference (MRD).  Results: Convergence of simulation methods in the same scenario reached in a few iterations using the proposed method.  Conclusion:  The  proposed  method  is  recommended  over  the  current  method  due  to  less  time consumption; this issue is important in studies with different scenarios. 
topic Data systems
Risk ratio
Causality
Computer simulation
Monte Carlo method
url https://jbe.tums.ac.ir/index.php/jbe/article/view/154
work_keys_str_mv AT kazemmohammad generationofdatawithspecificmarginalriskdifference
AT mohammadalimansournia generationofdatawithspecificmarginalriskdifference
AT safooragharibzadeh generationofdatawithspecificmarginalriskdifference
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