Topic: Why Joint Modeling

What Observed Follow-Up Can Reveal About Dropout

When Follow-Up Loss Becomes Informative

When Separate Longitudinal and Event Analyses Give Conflicting Results

Why Baseline Adjustment Does Not Remove Time-Varying Endogeneity

Why Complete-Case Comparisons Distort Progression

Why Last Observation Carried Forward Fails Under Attrition

Treatment Changes Driven by Biomarker History

Unobserved Health as a Confounder of Biomarker-Event Associations

Survivor Averages Versus Cohort Trajectories

Selection Bias Among Participants Who Remain Under Observation