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] |