Author:
Hanff Anne-Marie,Krüger Rejko,McCrum Christopher,Ley Christophe, ,Acharya Geeta,Aguayo Gloria,Alexandre Myriam,Ali Muhammad,Ammerlann Wim,Arena Giuseppe,Bassis Michele,Batutu Roxane,Beaumont Katy,Béchet Sibylle,Berchem Guy,Bisdorff Alexandre,Boussaad Ibrahim,Bouvier David,Castillo Lorieza,Contesotto Gessica,de Bremaeker Nancy,Dewitt Brian,Diederich Nico,Dondelinger Rene,Ramia Nancy E.,Ferrari Angelo,Frauenknecht Katrin,Fritz Joëlle,Gamio Carlos,Gantenbein Manon,Gawron Piotr,georges Laura,Ghosh Soumyabrata,Giraitis Marijus,Glaab Enrico,Goergen Martine,de Lope Elisa Gómez,Graas Jérôme,Graziano Mariella,Groues Valentin,Grünewald Anne,Hammot Gaël,Hansen Linda,Heneka Michael,Henry Estelle,Henry Margaux,Herbrink Sylvia,Herzinger Sascha,Hundt Alexander,Jacoby Nadine,Jónsdóttir Sonja,Klucken Jochen,Kofanova Olga,Lambert Pauline,Landoulsi Zied,Lentz Roseline,Longhino Laura,Lopes Ana Festas,Lorentz Victoria,Marques Tainá M.,Marques Guilherme,Conde Patricia Martins,May Patrick,Mcintyre Deborah,Mediouni Chouaib,Meisch Francoise,Mendibide Alexia,Menster Myriam,Minelli Maura,Mittelbronn Michel,Mtimet Saïda,Munsch Maeva,Nati Romain,Nehrbass Ulf,Nickels Sarah,Nicolai Beatrice,Nicolay Jean-Paul,Noor Fozia,Gomes Clarissa P. C.,Pachchek Sinthuja,Pauly Claire,Pauly Laure,Pavelka Lukas,Perquin Magali,Pexaras Achilleas,Rauschenberger Armin,Rawal Rajesh,Bobbili Dheeraj Reddy,Remark Lucie,Richard Ilsé,Roland Olivia,Roomp Kirsten,Rosales Eduardo,Sapienza Stefano,Satagopam Venkata,Schmitz Sabine,Schneider Reinhard,Schwamborn Jens,Severino Raquel,Sharify Amir,Soare Ruxandra,Soboleva Ekaterina,Sokolowska Kate,Theresine Maud,Thien Hermann,Thiry Elodie,Loo Rebecca Ting Jiin,Trouet Johanna,Tsurkalenko Olena,Vaillant Michel,Vega Carlos,Boas Liliana Vilas,Wilmes Paul,Wollscheid-Lengeling Evi,Zelimkhanov Gelani
Abstract
Abstract
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.
Funder
Fonds National de la Recherche Luxembourg
Publisher
Springer Science and Business Media LLC