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Table 1 Analogy between clinical research and computational statistical research

From: Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies

  Clinical research Computational statistical research
Trial type In vitro/animal study Simulation
  Clinical trial Benchmark study
  Blinded Neutral and blind analysis
  (Placebo) controlled (Null-model) controlled
  Cross-over Paired samples
  Multi-arm Multiple methods
Investigators Trialist Researcher conducting benchmark experiment
  Medical researcher Methodological researcher in computational statistics
  Sponsor Methodological researcher in computational statistics
Observation unit Clinical trial patients Real datasets
Comparators Therapies, interventions and controls Statistical and machine learning methods
Problem Treatment of medical condition Answering a question using data, e.g. prediction problem
Context Patient’s preference, social context Substantive context
  Personalized medicine Meta-learning
Objectives Improving patient’s health Yielding reliable answer, e.g. increasing prediction performance
  Selecting and applying therapy to patient Selecting and applying methods to datasets
  Application by medical practitioner Application by statistical practitioner/consultant
Endpoints Relevant clinical endpoints Error rate, AUC, computing time, etc.
  Missing value (e.g. dropout) Failure to produce output