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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Tehran University of Medical Sciences
2018-07-01
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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 |
_version_ |
1724399434155950080 |