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Interval-cohort designs and bias in the estimation of per-protocol effects: a simulation study.

Jessica G YoungRajet VatsaEleanor J MurrayMiguel A Hernán
Published in: Trials (2019)
Bias that arises from interval measurement designs highlights the need for planning in the design of randomized trials for collection of time-varying covariate data. This may come from more frequent in-person measurement or external sources (e.g., electronic medical record data). Such planning will provide improved estimates of the per-protocol effect through the use of methods that appropriately adjust for time-varying confounders.
Keyphrases
  • electronic health record
  • randomized controlled trial
  • big data
  • drinking water
  • data analysis
  • machine learning
  • artificial intelligence
  • finite element analysis
  • deep learning