Clinical trial optimization of efficacy studies in slowly progressive diseases
Ruben Faelens, Philippe Jacqmin, Per Olsson Gisleskog, Andreas Lindauer, Daniel Roeshammar
SGS Exprimo NV
Objectives: Treatments in slowly progressive diseases, such as Parkinson’s or Alzheimer’s disease, fall into two categories. Symptomatic treatment will treat the symptoms, while disease-modifying treatment can slow down or even reverse the disease.
Proving the disease-modifying action of a drug is difficult [1]. There is a high variability in disease progression, requiring a high number of patients to detect the effect. The study should also last long enough. This may increase dropout rates. Novel trial designs have been proposed (delayed start, wash-out) [2] [3], but are difficult to implement.
Clinical trial simulation can be a powerful tool to evaluate and improve various study designs, in light of these difficult criteria.
Methods: A literature model [3] was implemented in Simulo clinical trial simulator [4]. Study design parameters (number of patients, study length, observation frequency) were explored, as well as sensitivity to model parameter values (treatment effect, time delay to maximum effect, dropout rate, natural disease progression rate). Three possible analysis methods were evaluated: t-test on endpoint, mixed-effect model repeated measurements (MMRM) and model-based analysis (MBA).
Results: Increasing the number of patients and study length increased the probability to detect a disease modifying effect (probability of success, PoS) for all analysis methods. Measuring the disease progression more frequently than every 3 months improved the PoS when using the MBA but not with the t-test or MMRM methods. A lower treatment effect resulted in a lower PoS. In case of slow onset of treatment effect, the model-based analysis cannot distinguish between a symptomatic and disease-modifying compound. Dropout rate influenced the PoS, but only lightly affected the MBA method. Finally, lower natural disease progression rates did not influence PoS, although this did require an adaptation of the analysis methods.
Overall, MBA performed significantly better than the t-test and MMRM analysis.
Conclusions: Clinical trial simulation is an essential tool to optimize expensive, long-running studies in slowly progressive diseases. These studies may also benefit from a more widespread use of model-based analysis techniques of results.
References:
[1] Ploeger, Bart, and Nick Holford. "Confirmation of symptomatic and disease modifying effects of levodopa using the ELLDOPA study." (2011).
[2] Ploeger, Bart, and Nick Holford. "Optimizing trial designs for distinguishing short (symptomatic) and long-term (protective) treatment effects from natural disease progression."
[3] Ploeger, Berend Arnold, and Nicholas HG Holford. "Washout and delayed start designs for identifying disease modifying effects in slowly progressive diseases using disease progression analysis." Pharmaceutical statistics 8.3 (2009): 225-238.
[4] Ruben Faelens, Quentin Leirens, Philippe Jacqmin. “Simulo: a new PK-PD-Disease model simulator.” PAGE 24 (2015) Abstr 3681 [www.page-meeting.org/?abstract=3681]
