Strategies
For strategies 1 and 2, bias in estimation of ACE increased with higher values of α and γ, and was worse when μ = 0 (Results of Applying the 3 Strategies to Data Generated Under Different Scenarios, With Abbreviations: MAR, missing at random; MCAR, missing completely at random; MCE, Monte Carlo error; MNAR, missing not at random; RMSE, root mean square error; 
Results of Applying the 3 Strategies to Data Generated Under Different Scenarios, With Abbreviations: MAR, missing at random; MCAR, missing completely at random; MCE, Monte Carlo error; MNAR, missing not at random; RMSE, root mean square error; 
The impact of collider bias induced by 
Strategy 3 shows unbiased estimates (within Monte Carlo error) in all cases, indicating a successful recovery of the causal effect in Selection bias appears to increase under the following conditions:Larger numbers of missing eligible individualsLarger values of A stronger effect modification of the causal effect of With fewer eligible participants lost to missingness, there is less missing data to drive a differentiation in the distributions of When In summary, strategy 3 is necessary in the case that missing data are noticeably MAR or MNAR. If that is not the case, a user may prefer the simpler strategies 1 and 2. Strategy 2 is the most precise, if this is preferred by the user, but one must account for the possibility of undercoverage if a CI is sought.