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Best practices in statistical computing
Author(s) -
Sanchez Ricardo,
Griffin Beth Ann,
Pane Joseph,
McCaffrey Daniel F.
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9169
Subject(s) - computer science , workflow , documentation , coding (social sciences) , quality assurance , code (set theory) , best practice , code review , key (lock) , source code , data science , process (computing) , quality (philosophy) , data quality , software engineering , data mining , static program analysis , database , programming language , computer security , software , philosophy , statistics , operations management , mathematics , economy , set (abstract data type) , management , epistemology , economics , service (business) , metric (unit) , software development
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.