Parkinson’s disease — a progressive movement disorder of the nervous system — affects more than 1.1 million people in the US (and over 11 million globally), with an estimated 90,000 new diagnoses each year, making it the second-most common neurodegenerative disease after Alzheimer’s disease.1,2  The prevalence and rise of Parkinson’s disease has led to robust investment in understanding and treating this disorder.3

Here, we provide a brief overview of Parkinson’s disease and discuss common endpoints used in clinical trials with an illustrative case study on how those endpoints may be analyzed.

An introduction to Parkinson’s disease

Parkinson’s disease is a progressive movement disorder of the nervous system.4 It causes nerve cells (neurons) in parts of the brain to weaken, become damaged, and die, leading to symptoms that include problems with movement, tremor, stiffness, and impaired balance. As symptoms progress, people with Parkinson’s disease (PD) may have difficulty walking, talking, or completing other simple tasks.

The rate of PD progression and the particular symptoms differ among individuals. The four primary/hallmark symptoms of PD are tremor, rigidity, bradykinesia, and postural instability.

Other problems related to PD may include mental and emotional health problems, speech changes, dementia or other cognitive problems, pain, and fatigue.

On and Off states/periods

The On state is when PD medications are effective and motor and non-motor symptoms are controlled. The Off state is when PD symptoms return between medication doses or in the morning before the first dose.

Measuring Parkinson’s disease severity: Two evaluation methods

MDS-UPDRS: Evaluating motor and non-motor symptoms

The MDS-UPDRS (Movement Disorder Society–Unified Parkinson’s Disease Rating Scale) was developed to evaluate various aspects of PD, including daily non-motor and motor experiences and motor complications.5, 6

It is the most frequently used outcome in clinical trials, though it can also be employed in the clinical setting. It consists of four parts with 50 items in total, with each item rating the impairment with scores from 0 (normal) to 4 (severe). A patient’s global impairment is calculated as the total sum of these scores, with a higher score indicating greater impairment. Missing values might be imputed by the worst-case value of 4 (severe) if sufficient items are scored, otherwise the total score is set to missing. Each part can be analyzed separately as well.

MDS-UPDRS:

Parts of the MDS-UPDRS can be assessed during the ON and OFF state to evaluate the differences between those two states.

PDQ-39: A patient-reported health status questionnaire

The PDQ-39 (Parkinson’s Disease Questionnaire) is a 39-item patient-reported measure that assesses Parkinson’s disease–specific health-related quality of life.7, 8

It requires the patient to grade how often he/she experienced difficulties over the past month. Each item is scored on a scale from 0 (never) to 4 (always or cannot do at all, if applicable), with lower scores indicating better status. Items are grouped into eight dimension subscales.

PDQ-39:

PDQ-39 subscale scores range from 0 to 100, with 0 representing perfect health for the dimension and 100 representing worst health for the dimension. A PDQ-39 total score — the PDQ-39 Summary Index (PDSI) — can be computed as the mean of the eight PDQ-39 subscale scores providing an overall score reflecting the impact of Parkinson’s on quality of life.

In case of missing values, a possible approach is to impute missing values with the mean of the available subscale items, if the number of missing values is smaller than 50% within the subscale.

LED (Levodopa Equivalent Dose)

The dose of antiparkinsonian medication is standardized to the LED in mg based on predefined conversion rates.

A confirmatory Parkinson’s study: Statistical analysis and adaptive design

Our team partnered with a large biotech and biomedical engineering company to conduct the statistical analysis of a multi-center, open-label (one-arm) adaptive confirmatory study that used a device providing deep brain stimulation for Parkinson’s patients. The efficacy and futility boundaries of the adaptive design were computed using Cytel’s East Horizon™ platform.

The study had the following endpoints:

• Primary endpoint: MDS-UPDRS (part III)
• Secondary and exploratory endpoints: Other parts of MDS-UPDRS, PDQ-39, Clinical Global Impression of Change (CGI), Schwab and England ADL (Activities of Daily Living), antiparkinsonian medication use

Statistical analysis and its challenges

MDS-UPDRS (part III) score, PDQ-39, and antiparkinsonian medication use were analyzed using the paired t-test and CGI was analyzed using the non-parametric Wilcoxon signed-rank test. The Schwab and England ADL scale was analyzed with an ANOVA.

The first challenge was to understand the differences between the Off and On states. We also had to deal with missing data. It was decided that the missing values on visit level would be imputed by the worst response observed among all participants (primary analysis), with sensitivity analyses employing the baseline observation carried forward (BOCF) and the multiple imputation (MI) using Markov chain Monte Carlo (MCMC) methods.

Another more challenging aspect was understanding and programming the antiparkinsonian medication use (analyzed as secondary endpoint), which is calculated in LED. For this task, a close collaboration with the sponsor’s medical experts was needed to define the conversion factors and handle correctly special cases of medication combinations.

An adaptive design with four interim analyses

The study was designed to include four interim analyses and one final analysis, using the Lan-DeMets group sequential method with the O’Brien-Flemming α-spending function and Pocock β-spending function. The O’Brien-Fleming boundaries preserve a nominal significance level at the final analysis that is close to that of a single test procedure, so it is very conservative for the earlier interim analysis.9 The Pocock β-spending function uses approximately equal cutoffs for each analysis.

The efficacy and futility boundaries were computed via Cytel’s EAST software, which is integrated into the East Horizon™ platform. For the interim analyses, the efficacy and futility boundaries had to be recalculated based on the actual sample sizes.

Final takeaways

Parkinson’s disease is a lifelong and progressive, degenerative multiple-symptom disease that affects millions worldwide. The treatment is highly individualized and depends on the disease stage and severity of motor and non-motor symptoms. When symptoms become bothersome, current therapies primarily focus on symptom management, with pharmacological options such as levodopa and dopamine agonists forming the cornerstone of care. For those whose symptoms don’t respond well to medication in later stages, advanced options like deep brain stimulation (DBS) offer hope, which can provide relief for tremors and reduce dyskinesias.

The adaptive design of the case study offered a flexible, efficient, and ethical approach without compromising the validity and integrity of the study, which is implemented in the East Horizon™ platform that offers a comprehensive tool for trial design during all stages of development.

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