Table 4.

Assumptions, Limitations, Strengths and Biases between different methods of analysis

Method	Assumptions	Limitations	Strengths	Bias
Complete Case Analysis	Participants with missing data are a random sample of those intended to be observed [15, 29]	Loss of statistical power [56] Prone to bias [29]	Automatically implemented by software Common method	Might be biased if participants with missing data are different to those with complete data [15]
Survival Analysis	LTF is unrelated to mortality	Most studies found assumption to be incorrect Survival is usually overestimated	Most common method Easy to perform
Inverse Probability Weights from Tracing	Those unsuccessfully traced have the same mortality as those successfully traced “outcomes are missing at random after accounting for available covariates” [22]	Tracing was done at the end of the 10 year follow up period on everyone Case-wise deletion if covariates are missing Tracing can be difficult and expensive Only as successful as your tracing success Loss of statistical power [56]	Common method in HIV studies Conceptually easy to understand Best employed for monotone missing data [29]	Biased estimate of effect size [56] Residual selection bias [22]
Multiple Imputation with Chained Equations	Missing are only randomly different from patients with same set of covariates	Relies on a good prediction model Susceptible to human error [29]	Use all observations Robust standard error Least biased estimates of effect size [56] Gains in precision of estimation of effects [15]	If data are not MCAR results might be biased away from the null [29]