Table 2.

General analysis approaches for longitudinal data

Method	Explanation of methodology	Missing data	Pros	Cons
Simple analyses	Compare the mean outcome across multiple time points between groups or compare the area under the longitudinal plot of outcome measures between groups	Completely random	Simple and well‐established methodology	Require complete data, cannot assess effect of time on outcome
Transition models	Compare response changes between consecutive time points between groups	Completely random	Simple analysis	Require complete data, cannot formally assess effect of time on outcome, cannot determine overall effect of treatment
Marginal models	Regression model to assess treatment effect average over the population over time and can adjust for other covariates in the models	Completely random	Flexible regression models, can adjust for other covariates, can accommodate missing data if it is missing completely at random	Assume that data are missing completely at random, population average interpretation
Mixed effects models	Regression model to assess treatment effect within each individual over time and can adjust for other covariates	Random	Flexible regression models, can model differential treatment effect over time, can accommodate missing data with less stringent assumptions	Assume that data are missing at random
Imputation (simple or multiple)	Missing values are imputed based on various assumptions	Not random	Flexible models to fill in missing outcome measures, complete data set with imputed values can be used in any methods used for complete data	Require careful considerations of assumptions used for imputation
Mixture (pattern) models	Overall treatment effects are estimated as an average of effects from the mix of different dropout patterns	Not random	Flexible and require less stringent missing data mechanism	Based on unverifiable assumptions, require careful considerations for model assumptions