Best practices in statistical computing
The world is becoming increasingly complex, both in terms of the rich sources of data we have access to and the statistical and computational methods we can use on data. These factors create an ever‐increasing risk for errors in code and the sensitivity of findings to data preparation and the execut...
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Published in | Statistics in medicine Vol. 40; no. 27; pp. 6057 - 6068 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken, USA
John Wiley & Sons, Inc
30.11.2021
Wiley Subscription Services, Inc |
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Abstract | The world is becoming increasingly complex, both in terms of the rich sources of data we have access to and the statistical and computational methods we can use on data. These factors create an ever‐increasing risk for errors in code and the sensitivity of findings to data preparation and the execution of complex statistical and computing methods. The consequences of coding and data mistakes can be substantial. In this paper, we describe the key steps for implementing a code quality assurance (QA) process that researchers can follow to improve their coding practices throughout a project to assure the quality of the final data, code, analyses, and results. These steps include: (i) adherence to principles for code writing and style that follow best practices; (ii) clear written documentation that describes code, workflow, and key analytic decisions; (iii) careful version control; (iv) good data management; and (v) regular testing and review. Following these steps will greatly improve the ability of a study to assure results are accurate and reproducible. The responsibility for code QA falls not only on individual researchers but institutions, journals, and funding agencies as well. |
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AbstractList | The world is becoming increasingly complex, both in terms of the rich sources of data we have access to and the statistical and computational methods we can use on data. These factors create an ever‐increasing risk for errors in code and the sensitivity of findings to data preparation and the execution of complex statistical and computing methods. The consequences of coding and data mistakes can be substantial. In this paper, we describe the key steps for implementing a code quality assurance (QA) process that researchers can follow to improve their coding practices throughout a project to assure the quality of the final data, code, analyses, and results. These steps include: (i) adherence to principles for code writing and style that follow best practices; (ii) clear written documentation that describes code, workflow, and key analytic decisions; (iii) careful version control; (iv) good data management; and (v) regular testing and review. Following these steps will greatly improve the ability of a study to assure results are accurate and reproducible. The responsibility for code QA falls not only on individual researchers but institutions, journals, and funding agencies as well. The world is becoming increasingly complex, both in terms of the rich sources of data we have access to and the statistical and computational methods we can use on data. These factors create an ever-increasing risk for errors in code and the sensitivity of findings to data preparation and the execution of complex statistical and computing methods. The consequences of coding and data mistakes can be substantial. In this paper, we describe the key steps for implementing a code quality assurance (QA) process that researchers can follow to improve their coding practices throughout a project to assure the quality of the final data, code, analyses, and results. These steps include: (i) adherence to principles for code writing and style that follow best practices; (ii) clear written documentation that describes code, workflow, and key analytic decisions; (iii) careful version control; (iv) good data management; and (v) regular testing and review. Following these steps will greatly improve the ability of a study to assure results are accurate and reproducible. The responsibility for code QA falls not only on individual researchers but institutions, journals, and funding agencies as well.The world is becoming increasingly complex, both in terms of the rich sources of data we have access to and the statistical and computational methods we can use on data. These factors create an ever-increasing risk for errors in code and the sensitivity of findings to data preparation and the execution of complex statistical and computing methods. The consequences of coding and data mistakes can be substantial. In this paper, we describe the key steps for implementing a code quality assurance (QA) process that researchers can follow to improve their coding practices throughout a project to assure the quality of the final data, code, analyses, and results. These steps include: (i) adherence to principles for code writing and style that follow best practices; (ii) clear written documentation that describes code, workflow, and key analytic decisions; (iii) careful version control; (iv) good data management; and (v) regular testing and review. Following these steps will greatly improve the ability of a study to assure results are accurate and reproducible. The responsibility for code QA falls not only on individual researchers but institutions, journals, and funding agencies as well. |
Author | McCaffrey, Daniel F. Sanchez, Ricardo Pane, Joseph Griffin, Beth Ann |
AuthorAffiliation | 4 Educational Testing Service, Princeton, New Jersey, USA 3 RAND Corporation, Pittsburgh, Pennsylvania, USA 2 RAND Corporation, Arlington, Virginia, USA 1 UnitedHealthcare, Minnetonka, Minnesota, USA |
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Cites_doi | 10.1038/s41586‐020‐2314‐9 10.1038/483531a 10.1093/aje/kwab091 10.1093/aje/kwab092 10.1186/s13059‐016‐1044‐7 10.1073/pnas.1602413113 10.1096/fj.14‐256735 10.1186/s12911‐019‐0983‐9 10.1126/science.aac4716 10.18637/jss.v074.i11 10.1016/j.jsat.2020.108075 10.1126/science.aan6937 10.1371/journal.pcbi.1005097 10.1080/00031305.2017.1375989 10.1038/530281c 10.1038/s41567‐018‐0342‐2 10.1038/scientificamerican1018‐56 10.1371/journal.pcbi.1005412 10.1257/aer.100.2.573 10.1080/00031305.2017.1399928 |
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SubjectTerms | Data analysis data management Humans Mathematical Computing Medical research methodology Quality control Research data management Statistical methods version control |
Title | Best practices in statistical computing |
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