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Complete case analysis is the term used to describe a statistical analysis that only includes participants for which we do not have missing data on the variables of interest

Explicit Single imputation denotes a method based on an explicit model which replaces a missing datum with a single value. In this method the sample size is retrieved. However, the imputed values are assumed to be the real values that would have been observed when the data would have been complete

Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a probability distribution by maximising a likelihood function, so that under the assumed statistical model the observed data is most probable

The inverse probability weighting (IPW) approach preserves the semiparametric structure of the underlying model of substantive interest and clearly separates the model of substantive interest from the model used to account for the missing data

Specific methods are required to make inference under nonignorable nonresponse assumptions, that is when the value of the variable that is missing is related to some values which are not observed by the analyst (e.g. the missing values themselves)

Multiple Imputation by Chained Equations (MICE) allows most models to be fit to a dataset with missing values on the independent and/or dependent variables, and provides rigorous standard errors for the fitted parameters. The basic idea is to treat each variable with missing values as the dependent variable in a regression, with some or all of the remaining variables as its predictors

Augmented Inverse Probability Weighting (AIPW) is a IPW technique that derives estimators using a combination of the propensity score and the regression model. This approach has the attractive doubly robust property that estimators are consistent as long as either the propensity score or the outcome regression model is correctly specified

Available-case analysis also arises when a researcher simply excludes a variable or set of variables from the analysis because of their missing-data rates

Implicit Single imputation denotes a method not based on an explicit model which replaces a missing datum with a single value. In this method the sample size is retrieved. However, the imputed values are assumed to be the real values that would have been observed when the data would have been complete

Bayesian inference is a method of statistical inference in which Bayes theorem is used to update the probability for a hypothesis as more evidence or information becomes available