Estimating bias from withdrawal of life sustaining treatment in clinical trials.

Withdrawal of life sustaining treatment (WLST) is common in critically ill patients and confers a high probability of in-hospital death. In clinical trials, WLST is a specific example of a post-randomization intervention with a strong influence on outcome that can bias estimates of treatment effect. Only under several strong assumptions do the observed effects from randomized treatment in the presence of WLST correspond to those expected in its absence. However, WLST is rarely accounted for in trials. We propose a systematic approach to analyze the rates of WLST and characteristics of trial participants who died after WLST in order to identify when bias is present, and sensitivity analyses to set bounds on the direction and magnitude of this bias. In an example randomized trial of treatments for out-of-hospital cardiac arrest, the bias attributable to WLST reduces the observed treatment effects. Sensitivity analyses set bounds on this bias and estimate hypothetical treatment effects that might have been observed in the absence of WLST. This approach provides better insight into the observed results of the trial.