Detecting placebo and drug effects on Parkinson's disease symptoms by longitudinal item‐score models - Chao Chen; Siv Jönsson; Shuying Yang; Elodie L. Plan; Mats O. Karlsson (Wiley, 2021)
Abstract This study tested the hypothesis that analyzing longitudinal item scores of the Unified Parkinson's Disease Rating Scale could allow a smaller trial size and describe a drug's effect on symptom progression. Two historical studies of the dopaminergic drug ropinirole were analyzed: a cross‐over formulation comparison trial in 161 patients with early‐stage Parkinson's disease, and a 24‐week, parallel‐group, placebo‐controlled efficacy trial in 393 patients with advanced‐stage Parkinson's disease. We applied item response theory to estimate the patients' symptom severity and developed a longitudinal model using the symptom severity to describe the time course of the placebo response and the drug effect on the time course. Similarly, we developed a longitudinal model using the total score. We then compared sample size needs for drug effect detection using these two different models. Total score modeling estimated median changes from baseline at 24 weeks (90% confidence interval) of −3.7 (−5.4 to −2.0) and −9.3 (−11 to −7.3) points by placebo and ropinirole. Comparable changes were estimated (with slightly higher precision) by item‐score modeling as −2.0 (−4.0 to −1.0) and −9.0 (−11 to −8.0) points. The treatment duration was insufficient to estimate the symptom progression rate; hence the drug effect on the progression could not be assessed. The trial sizes to detect a drug effect with 80% power on total score and on symptom severity were estimated (at the type I error level of 0.05) as 88 and 58, respectively. Longitudinal item response analysis could markedly reduce sample size; it also has the potential for assessing drug effects on disease progression in longer trials.