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INTEREST: INteractive Tool for Exploring REsults from Simulation sTudies
Applications · Submitted 9 Sep 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In conclusion, INTEREST can facilitate the investigation of results from simulation studies and supplement the reporting of results, allowing researchers to share detailed results from their simulations and readers to explore them freely.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
Simulation studies allow us to explore the properties of statistical methods. They provide a powerful tool with a multiplicity of aims; among others: evaluating and comparing new or existing statistical methods, assessing violations of modelling assumptions, helping with the understanding of statistical concepts, and supporting the design of clinical trials. The increased availability of powerful computational tools and usable software has contributed to the rise of simulation studies in the current literature. However, simulation studies involve increasingly complex designs, making it difficult to provide all relevant results clearly. Dissemination of results plays a focal role in simulation studies: it can drive applied analysts to use methods that have been shown to perform well in their settings, guide researchers to develop new methods in a promising direction, and provide insights into less established methods. It is crucial that we can digest relevant results of simulation studies. Therefore, we developed INTEREST: an INteractive Tool for Exploring REsults from Simulation sTudies. The tool has been developed using the Shiny framework in R and is available as a web app or as a standalone package. It requires uploading a tidy format dataset with the results of a simulation study in R, Stata, SAS, SPSS, or comma-separated format. A variety of performance measures are estimated automatically along with Monte Carlo standard errors; results and performance summaries are displayed both in tabular and graphical fashion, with a wide variety of available plots. Consequently, the reader can focus on simulation parameters and estimands of most interest. In conclusion, INTEREST can facilitate the investigation of results from simulation studies and supplement the reporting of results, allowing researchers to share detailed results from their simulations and readers to explore them freely.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Simulation studies allow us to explore the properties of statistical methods. | background 1.00 | 0.04 | 0.02 | asserted 0.97 |
| s1 | They provide a powerful tool with a multiplicity of aims; among others: evaluating and comparing new or existing statistical methods, assessing violations of modelling assumptions, helping with the understanding of statistical concepts, and supporting the design of clinical trials. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | The increased availability of powerful computational tools and usable software has contributed to the rise of simulation studies in the current literature. | background 1.00 | 0.05 | 0.02 | asserted 0.99 |
| s3 | However, simulation studies involve increasingly complex designs, making it difficult to provide all relevant results clearly. | gap 0.51 | 0.07 | 0.02 | asserted 1.00 |
| s4 | Dissemination of results plays a focal role in simulation studies: it can drive applied analysts to use methods that have been shown to perform well in their settings, guide researchers to develop new methods in a promising direction, and provide insights into less established methods. | background 0.95 | 0.06 | 0.04 | asserted 0.80 |
| s5 | It is crucial that we can digest relevant results of simulation studies. | background 0.69 | 0.05 | 0.05 | asserted 1.00 |
| s6 | Therefore, we developed INTEREST: an INteractive Tool for Exploring REsults from Simulation sTudies. | contribution 1.00 | 0.95 | 0.20 | asserted 1.00 |
| s7 | The tool has been developed using the Shiny framework in R and is available as a web app or as a standalone package. | method 0.85 | 0.87 | 0.03 | asserted 1.00 |
| s8 | It requires uploading a tidy format dataset with the results of a simulation study in R, Stata, SAS, SPSS, or comma-separated format. | method 0.98 | 0.49 | 0.02 | asserted 1.00 |
| s9 | A variety of performance measures are estimated automatically along with Monte Carlo standard errors; results and performance summaries are displayed both in tabular and graphical fashion, with a wide variety of available plots. | method 0.89 | 0.80 | 0.04 | asserted 1.00 |
| s10 | Consequently, the reader can focus on simulation parameters and estimands of most interest. | interpretation 0.85 | 0.23 | 0.06 | asserted 0.91 |
| s11 | In conclusion, INTEREST can facilitate the investigation of results from simulation studies and supplement the reporting of results, allowing researchers to share detailed results from their simulations and readers to explore them freely. | interpretation 0.51 | 0.49 | 0.95 | hedged possibility 0.78 |
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The paper
Original abstract
Simulation studies allow us to explore the properties of statistical methods. They provide a powerful tool with a multiplicity of aims; among others: evaluating and comparing new or existing statistical methods, assessing violations of modelling assumptions, helping with the understanding of statistical concepts, and supporting the design of clinical trials. The increased availability of powerful computational tools and usable software has contributed to the rise of simulation studies in the current literature. However, simulation studies involve increasingly complex designs, making it difficult to provide all relevant results clearly. Dissemination of results plays a focal role in simulation studies: it can drive applied analysts to use methods that have been shown to perform well in their settings, guide researchers to develop new methods in a promising direction, and provide insights into less established methods. It is crucial that we can digest relevant results of simulation studies. Therefore, we developed INTEREST: an INteractive Tool for Exploring REsults from Simulation sTudies. The tool has been developed using the Shiny framework in R and is available as a web app or as a standalone package. It requires uploading a tidy format dataset with the results of a simulation study in R, Stata, SAS, SPSS, or comma-separated format. A variety of performance measures are estimated automatically along with Monte Carlo standard errors; results and performance summaries are displayed both in tabular and graphical fashion, with a wide variety of available plots. Consequently, the reader can focus on simulation parameters and estimands of most interest. In conclusion, INTEREST can facilitate the investigation of results from simulation studies and supplement the reporting of results, allowing researchers to share detailed results from their simulations and readers to explore them freely.
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