  Programming is ubiquitous in applied biostatistics; adopting software
engineering skills will help biostatisticians do a better job. To explain this,
we start by highlighting key challenges for software development and
application in biostatistics. Silos between different statistician roles,
projects, departments, and organizations lead to the development of duplicate
and suboptimal code. Building on top of open-source software requires critical
appraisal and risk-based assessment of the used modules. Code that is written
needs to be readable to ensure reliable software. The software needs to be
easily understandable for the user, as well as developed within testing
frameworks to ensure that long term maintenance of the software is feasible.
Finally, the reproducibility of research results is hindered by manual analysis
workflows and uncontrolled code development. We next describe how the awareness
of the importance and application of good software engineering practices and
strategies can help address these challenges. The foundation is a better
education in basic software engineering skills in schools, universities, and
during the work life. Dedicated software engineering teams within academic
institutions and companies can be a key factor for the establishment of good
software engineering practices and catalyze improvements across research
projects. Providing attractive career paths is important for the retainment of
talents. Readily available tools can improve the reproducibility of statistical
analyses and their use can be exercised in community events. [...]
