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Table 4 Assumptions, Limitations, Strengths and Biases between different methods of analysis

From: Treating loss-to-follow-up as a missing data problem: a case study using a longitudinal cohort of HIV-infected patients in Haiti

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]