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Non-linearity of Parkinson’s disease progression: implications for sample size calculations in clinical trials

Dept of Biostatistics, Bioinformatics & Epidemiology, Medical University of South Carolina, Charleston, SC, USA

Karl Kieburtz

Dept of Neurology, University of Rochester, Clinical Trials Coordination Center, Rochester, NY, USA

Christopher G Goetz

Dept of Neurological Sciences, Rush University’s Medical Center, Chicago, IL, USA

Jordan J Elm

Yuko Y Palesch

Peng Huang

Dept of Biostatistics, Bioinformatics & Epidemiology, Medical University of South Carolina, Charleston, SC, USA

Bernard Ravina

NINDS, National Institutes of Health, Neuroscience Center, Bethesda, MD, USA

Caroline M Tanner

The Parkinson’s Institute, 1170 Morse Avenue, Sunnyvale CA, USA

Barbara C Tilley

Dept of Biostatistics, Bioinformatics & Epidemiology, Medical University of South Carolina, Charleston, SC, USA

Background Estimation of sample size for long-term studies of neuroprotection in Parkinson’s disease requires information on expected clinical decline. Values may be obtained by analyzing existing long-term data sets or by prediction models of clinical decline applied to available data from shorter-term trials. The most commonly used measure to track clinical decline is the Unified Parkinson’s Disease Rating Scale (UPDRS) but this measure is also affected by symptomatic therapy. Models can help better understand behavior of the UPDRS after initiation of symptomatic therapy when scores will improve and eventually start deteriorating again.

Purpose To understand how UPDRS scores progress after initiation of symptomatic therapy and how this progression impacts sample size calculations.

Methods We developed a non-linear model of UPDRS after introduction of symptomatic therapy. The model is specified as a non-linear mixed effects model and is applied to three different data sets from clinical trials. The model is then used to produce estimates for the change in UPDRS and its associated variance for a period of up to five years of follow-up. The estimates produced by the model serve as the basis for sample-size calculations for different lengths of follow-up (one through five years) and for different values of clinically meaningful change in UPDRS.

Results Despite differences in the short-term benefit of the dopaminergic drugs, after a period of approximately six months UPDRS scores progress linearly at an estimated rate of approximately three points a year. The sample size that is required for a clinical trial where the baseline coincides with initiation of symptomatic therapy is very large. On the other hand, if baseline is set at six months after initiation of symptomatic therapy then the sample size required decreases with length of follow-up.

Limitations Model specification and estimation is based on a set of simplifying assumptions regarding the progression of individual level UPDRS scores.

Conclusions Sample size calculations based on these estimates indicate a substantial reduction in sample size if patients are required to be on symptomatic treatment for a period of time before being randomized to a neuroprotective trial.

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