Background
Questions that can be addressed by a longitudinal study
Special considerations in analyzing the longitudinal study
Juvenile dermatomyositis as a disease model for longitudinal analysis
Questions and answers
Question 1: What is the disease activity course for a population of JDM patients?
Question 2a: How do we determine the disease activity course of an individual with JDM?
Question 2b: How do we predict the disease activity course of an individual with JDM?
Question 3a: Are there different patterns (or subgroups) of disease activity course among individuals with JDM?
Question 3b: What factors predict an individual’s membership in the different subgroups of disease activity?
Question 4a: What are the separate disease activity courses for the skin and musculoskeletal components of JDM and what is the relationship between these two disease components?
Question 4b: What factor(s) predicts the joint disease activity courses of the skin and musculoskeletal components of disease in an individual with JDM?
Predictor | Outcome | Predictor Estimate | Standard Error |
p
|
---|---|---|---|---|
bDAS | MDAS | 0.0363 | 0.0316 | 0.25 |
SDAS | 0.0214 | 0.0342 | 0.53 | |
Timea*bDAS | MDAS | 1.0140 | 0.1321 |
<0.0001
|
SDAS | 0.0362 | 0.1261 | 0.77 | |
Steroidb
| MDAS | -0.0919 | 0.0753 | 0.22 |
SDAS | 0.1211 | 0.0688 | 0.08 |
Conclusions
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 estimates45
|
LCTAa
| 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 Modelb
| 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. |