Table 2.

Overview of ML estimation methods

Method	Indications for Use	Missing-Data Mechanism	Implementation
Complete case analysis	Missing in outcome depends on randomization group	Ignorable	Compute simple statistics for complete cases (participants not missing the outcome).
Complete case analysis with covariate adjustment	Missing in outcome depends on randomization group and covariate	Ignorable after covariate adjustment	Fit the outcome model (as a function of randomization group and covariates) to complete cases with the covariate.
Survival analysis with covariate adjustment	Censoring depends on randomization group and covariate	Ignorable after covariate adjustment	Fit the outcome model (as a function of randomization group and covariates) to survival data.
Analysis via propensity-to-be-missing scores	Missing in outcome or censoring depends on randomization group and many covariates	Ignorable after covariate adjustment	(1) Fit a model for the missing-data mechanism. (2) Use the fitted model to compute scores. (3) Compute overall estimate based on quintiles of scores.
Longitudinal dropout analysis	Dropout depends on previous observed outcome and possibly randomization group and covariate	Ignorable	For a continuous longitudinal outcome, fit a marginal model using commercial software. For a longitudinal binary outcome, fit a conditional model.
Perfect fit analysis	Saturated models with categorical data	Ignorable or nonignorable	(1) Set expected counts equal to observed counts and solve for parameter estimates. (2) Compute statistic from parameter estimates. (3) Compute estimated variance using MP transformation.
Composite linear models	Flexible models with categorical data	Ignorable or nonignorable	Fit using specialized software.