Statistical methods for analyzing change over time of continuous outcomes in observational health studies: a scoping...

Analyses of within-person change in continuous outcomes are widely used in longitudinal observational health research. However, modeling change presents challenges, particularly regarding questions about when adjustment for baseline outcome values is indicated and how to deal with regression to the mean (RTM) and measurement error (ME). Despite extensive methodological debate, it remains unclear how these issues are addressed in applied studies and what analytical strategies have been proposed to mitigate related biases.

We conducted a scoping review of methodological studies proposing strategies to address bias when analyzing within-person change in continuous outcomes and of applied observational studies to examine analytical practice following PRISMA guidelines and PROSPERO registration (CRD420251024543). MEDLINE and EMBASE were searched from January 2000 to April 2025. Methodological studies addressed RTM, ME, or related modeling challenges while applied studies examined longitudinal changes in continuous metabolic syndrome (MetS) indicators as well as body weight outcomes. Findings were synthesized narratively and as a decision framework to help researchers select analytical strategies according to the bias of interest.

We included 66 methodological and 102 applied studies. Methodological studies proposed a range of approaches to address bias related to RTM and ME, including analytical RTM correction methods, model-based longitudinal approaches, alternative estimators for baseline-change associations, causal and estimand-based frameworks, calibration methods, latent-variable models, and flexible ME models. Common recommendations were aligning analyses with the research question, accounting for measurement processes, and using repeated measurements where possible. In contrast, applied studies showed heterogeneity in analytical practice. Forty-nine percent used models adjusting for baseline outcome values, 44% used models without baseline adjustment, and 7% reported both. Baseline adjustment was more common in studies examining body weight compared to MetS indicators. Justification for modeling choices, use of causal tools, and addressing related biases was uncommon.

The methodological literature provides a diverse set of approaches addressing RTM and ME in analyses of within-person change, but many of these considerations are not routinely reflected in applied observational research. By synthesizing methodological guidance and comparing it with current analytical practice, this review provides a structured framework to support more transparent and methodologically informed analyses of longitudinal change.