Mixed effects models but not t-tests or linear regression detect progression of apathy in Parkinson’s disease over seven years in a cohort: a comparative analysis

• Published in: BMC medical research methodology Vol. 24; no. 1; pp. 183 - 8
• Main Authors: Hanff, Anne-Marie, Krüger, Rejko, McCrum, Christopher, Ley, Christophe
• Format: Journal Article
• Language: English
• Published: London BioMed Central 24.08.2024; BioMed Central Ltd; BMC
• Subjects: Aged; Apathy; Apathy - physiology; Care and treatment; Cohort studies; Data analysis; Diagnosis; Disease Progression; Epidemiology; Estimates; Evaluation; Female; Health Sciences; Humans; Information management; Linear Models; Longitudinal Studies; Lost to follow-up; Male; Medical research; Medicine; Medicine & Public Health; Medicine, Experimental; Middle Aged; Models, Statistical; Parkinson; Parkinson Disease - diagnosis; Parkinson Disease - physiopathology; Parkinson Disease - psychology; Parkinson's disease; Regression analysis; Retrospective Studies; Statistical analysis; Statistical methods; Statistical model; Statistical Theory and Methods; Statistics for Life Sciences; Theory of Medicine/Bioethics; Luxembourg; Lost to follow-up; Epidemiology; Statistical model; Parkinson; Cohort studies; Disease progression
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-024-02301-7

Introduction While there is an interest in defining longitudinal change in people with chronic illness like Parkinson’s disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers. Here, we aim to demonstrate how the choice of statistical method may influence research outcomes, (e.g., progression in apathy), specifically the size of longitudinal effect estimates, in a cohort. Methods In this retrospective longitudinal analysis of 802 people with typical Parkinson’s disease in the Luxembourg Parkinson's study, we compared the mean apathy scores at visit 1 and visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers and the apathy score using linear regression and longitudinal two-level mixed effects models. Results Mixed effects models were the only method able to detect progression of apathy over time. While the effects estimated for the group comparison and the linear regression were smaller with high p -values (+ 1.016/ 7 years, p  = 0.107, -0.056/ 7 years, p  = 0.897, respectively), effect estimates for the mixed effects models were positive with a very small p -value, indicating a significant increase in apathy symptoms by + 2.345/ 7 years ( p  < 0.001). Conclusion The inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change. While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome, they are worth considering for longitudinal data analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation.