Table 2.

Outline of Potential Issues by Analytic Method

Analytic Method	Potential Issues to Consider
Complete case analysis	Not recommended
	Least rigorous approach to addressing missingness
	Produces biased estimates of cost and effectiveness if data are MAR and MNAR
	Reduces statistical power in analysis
	Complete case resampling is not recommended in generating cost-effectiveness acceptability curves.
Carry-forward imputation	Rarely recommended
	Produces biased estimates of cost and effectiveness if data are MAR and MNAR
	Imposes relatively strong assumptions on the longitudinal values of missing data
	Introduces bias if trial data are affected by differential attrition or missingness between treatment arms
	Introduces bias if cost and effectiveness data are correlated
	No standard procedure recommended in combining carry-forward imputation with the nonparametric bootstrap
Linear imputation	Rarely recommended
	Only applicable for nonmonotonically missing longitudinal data
	Produces biased estimates of cost and effectiveness if data are MAR and MNAR
	Assumes missing data evolve linearly between nonmissing data points
	Introduces bias if cost and effectiveness data are correlated
	No standard procedure recommended in combining linear imputation with the nonparametric bootstrap
Single-regression imputation	Rarely recommended
	Requires “correct” imputation model to be identified
	Does not account for uncertainty in imputed values of missing data
	May over or underestimate the correlation between cost and effectiveness variables
	No standard procedure recommended in combining single regression imputation with the nonparametric bootstrap
Inverse probability weighting	Standard 2-step estimation procedure does not guarantee a balanced sample.
	Requires compete data on all covariates to estimate the initial probability model
	Propensity scores may exhibit high variance.
	Probability models (e.g., logit or probit) may be affected by partial separation when dichotomous explanatory variables are highly correlated with incomplete cases.
Multiple imputation	Requires “correct” imputation model to be identified
	No standard method recommended to ensure that each multiply imputed data set within the nonparametric bootstrap is valid
	No standard procedure recommended in combining multiple imputation with the nonparametric bootstrap
	Computation efficiency of multiple imputation embedded within resampling limits its application.

Abbreviations: MAR, missing at random; MNAR, missing not at random.