DISCUSSION
DISEASES, APPENDIX
In this paper we bring to light notable sources of bias in target trial emulation, emanating from ignoring missing eligible data. We explored one means to analyze a TT combined with multiple imputation of eligibility criteria prior to selection. We demonstrated via simulation that an imputed TT can eliminate sources of selection and collider bias, improve the sample size of a TT, and allow users to investigate sensitivity to changes in the assumptions of the missing eligible data on effect size.An imputed TT of the effect of receiving at least 1 dose of palivizumab on RSV-related hospital admission indicated that a significant number of infants with missing gestational age were eligible, although any selection bias in this case was small.We identified characteristics of the data that determine the size of selection bias, namely the strength of the MAR or MNAR mechanism, the number of missing eligible individuals and the size of the effect modification. None of these characteristics can be calculated from the source population but could be inferred using external linked data sets. This selection bias can occur if any variable related to eligibility is an effect modifier. We showed in Web Appendix 1 that when A limitation of this method is the tendency of CIs to overcover. The Boot-MI method (Instead of MI, we could consider using inverse probability weighting to address the bias caused by missingness in It is also worth noting that using strategy 2, and targeting the causal effect in those with complete records, may be a pragmatic choice if the expected selection bias is limited and the source population is cumbersome.Data on palivizumab prescriptions and adherence were limited, and this had an impact on the quality of conclusions that could be made. Clinical colleagues reassure us that children hospitalized with RSV would not be issued palivizumab, protecting from reverse causation. However, other issues, such as confounding by indication, cannot be discounted. Limitations of the diagnostic data also meant a slight inflation of our definition of the eligible population because some of the diagnoses may include less severe diseases than those listed in the Green Book (