A-14 Propensity Scores in Neuropsychological Research: Four Aspects of Digital Clock Drawing Distinguish Individuals with Non-Dementia Idiopathic Parkinson’s Disease from Matched Controls

Abstract

Abstract Objective In situations in which randomized experiments are impossible or unethical, propensity score matching offers a method to reduce bias on causal effect estimates (Thoemmes & Kim, 2011). In this study, we examined differences on the digital clock drawing test (dCDT; Souillard-Mandar et al., 2016) between individuals with idiopathic non-dementia Parkinson’s disease (PD) and matched controls. Method This study involved a retrospective analysis of two federally funded investigations (NSF-13-543; R01-NS082386). The sample included 261 participants (110 PD, 151 non-PD). Participants were matched according to demographic covariates, as well as measures of mood, comorbidity, and premorbid functioning. The PD group and matched controls were compared using logistic regression in a Bayesian framework, with projection predictive variable selection implemented to obtain a parsimonious model (Piironen, Paasiniemi, & Vehtari, 2018). All effects were standardized. Results Of 261 participants, 212 were matched using nearest neighbor matching (Figure 1). The final, parsimonious model included four variables from the dCDT: total strokes (command condition), total time (command condition), and area (command and copy conditions). While all effects were retained, positive to strong evidence was found for dCDT total time (βMedian = 0.91, βSD = 0.25, 95% CI [0.44, 1.42], Bayes factor [BF] = 97.80) and dCDT area (copy condition; βMedian = −0.52, βSD = 0.19, 95% CI [−0.90, −0.17], BF = 4.78). Conclusions Propensity scores can be employed in causal comparative studies to match control participants and reduce bias from nuisance covariates. Four aspects of dCDT performance were optimal in distinguishing individuals with PD from matched controls.