Model | Questions | Advantages | Disadvantages |
---|---|---|---|
GEE | What is the averaged outcome trajectory for the population? (Trajectory of averages) |
Parameter estimates robust to misspecification of the covariance structure. Both time-invariant and time-varying predictors can be studied. |
No individual level inference Assumes missing data to be missing completely at random (MCAR), which may not be true for many longitudinal studies. |
MRM |
What is the outcome trajectory of the individual? What is the average outcome trajectory for the population? (Average of trajectories) |
Individual level inference possible with the incorporation of random effects. Both time-invariant and time-varying predictors can be studied. Assumes missing data to be missing at random (MAR), which is more likely in longitudinal studies. | Misspecification of covariance structure may bias parameter estimates^{45} |
LCTA^{a} |
Are there distinct subgroups within the study population? What are the trajectories of the identifiable subgroups within the population? |
Objectively identifies latent distinct subgroups within a heterogenous population. Able to use time-invariant factors to predict group membership. Able to study effects of time-varying covariates in different ways (depending on question and underlying theoretical framework) Assumes data to be missing at random (MAR). | Complex and time-consuming computing procedures. Interpretation of time-varying covariates can be challenging depending on the formulation. |
Joint Model^{b} |
What are the trajectories of (multiple) outcomes of interest? What is the correlation between the outcome trajectories of interest (i.e., are the trajectories concordant or discordant)? |
Multiple outcome trajectories of disparate nature (e.g., continuous with binary, binary-poisson, continuous-survival) can be studied simultaneously. Objective determination of the longitudinal correlation of the trajectories. Joint model with time-to-dropout may be used as a means to adjust for data missing not at random (MNAR). | Modeling procedures can be complex with increasing number and kinds of outcomes modeled jointly. |