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.

Abstract

OBJECTIVE 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.STUDY DESIGN AND SETTING In this retrospective longitudinal analysis of 802 people with typical Parkinson’s disease in the Luxembourg Parkinson's study, we compared the mean apathy score at visit 1 with the mean apathy score at visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers (all observations) and the apathy score (change in apathy per year) 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/ 7years, p = 0.107, -0.008/ year, p = 0.897, respectively), indicating an insignificant change in apathy over time, effect estimates for the mixed effects models were positive with a very small p-value, indicating a significant increase in apathy symptoms per year by +0.335 (p < 0.001). We provided evidence for, and theoretical explanations of, how mixed effects models can be used to assess symptoms progression more reliably, as well as the limitations of group comparison and linear regression in the analysis of longitudinal data.CONCLUSION Mixed effects models can be used to estimate different types of longitudinal effects while 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. Thus, researchers should rather consider mixed effects models for longitudinal analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation of results of cohort studies.