Pitfalls and potentials in simulation studies: Questionable research practices in comparative simulation studies allow for spurious claims of superiority of any method

Abstract

Comparative simulation studies are workhorse tools for benchmarking statistical methods. As with other empirical studies, the success of simulation studies hinges on the quality of their design, execution and reporting. If not conducted carefully and transparently, their conclusions may be misleading. In this paper we discuss various questionable research practices which may impact the validity of simulation studies, some of which cannot be detected or prevented by the current publication process in statistics journals. To illustrate our point, we invent a novel prediction method with no expected performance gain and benchmark it in a pre-registered comparative simulation study. We show how easy it is to make the method appear superior over well-established competitor methods if questionable research practices are employed. Finally, we provide concrete suggestions for researchers, reviewers and other academic stakeholders for improving the methodological quality of comparative simulation studies, such as pre-registering simulation protocols, incentivizing neutral simulation studies and code and data sharing.

References in corpus (1)

• Phases of methodological research in biostatistics - building the evidence base for new methods

Cited by in corpus (6)

• Phases of methodological research in biostatistics - building the evidence base for new methods
• Explaining the optimistic performance evaluation of newly proposed methods: a cross-design validation experiment
• On "Confirmatory" Methodological Research in Statistics and Related Fields
• Rethinking the handling of method failure in comparison studies
• Multilevel Metamodels: Enhancing Inference, Interpretability, and Generalizability in Monte Carlo Simulation Studies
• Outcomes truncated by death in RCTs: a simulation study on the survivor average causal effect