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A comparison of methods to estimate the survivor average causal effect in the presence of missing data: a simulation study.

Myra B McGuinessJessica KaszaAmalia KarahaliosRobyn H GuymerRobert P FingerJulie A Simpson
Published in: BMC medical research methodology (2019)
On average, MSMs with weighting for exposure, missing data and survival produced biased estimates of the SACE in the presence of an unmeasured survival-outcome confounder. The direction and magnitude of effect of unmeasured survival-outcome confounders should be considered when assessing exposure-outcome associations in the presence of attrition due to death.
Keyphrases
  • free survival
  • electronic health record
  • big data
  • machine learning
  • artificial intelligence
  • deep learning