gms | German Medical Science

Background: Proper methodological planning and excellent logistics is the key to success or failure of clinical trials. They also have a major impact on total study costs. In this presentation, I will illustrate the impact of biostatistical planning on clinical trials using three randomized controlled trials as examples.

Methods: The first example deals with precision medicine. Aim of the trial is to show a gradient in response by genetic status to treatment with coenzyme Q10 (CoQ10) in patients with Parkinson's disease. Patients are either randomized to high dose CoQ10 or placebo. The genetic status is assumed to have a gradient, and four different genetic constellations are considered. The challenge in this trial is the creation of a proper statistical hypothesis and analysis model. In the second example, I consider a randomized controlled trial for comparing insulin pumps with standard insulin injections in children and adults with type 1 diabetes. The challenge in this trial was that the required sample size could not be achieved within the planned and funded recruitment period, and I illustrate the replanning of the trial. In the third example, I consider biodegradable magnesium-based screws which are modern alternatives to conventional metal implants, made of titanium or steel, or absorbable polymer-based implants. They are in use for various surgical procedures. The challenge in this trial is the adequate choice of the primary endpoints for investigating non-inferiority of the biodegradable implants when compared with conventional metal implants. In this example, we consider practical aspects on the choice of the primary endpoint.

Results: In the first example, we propose a slope test for the analysis of the primary endpoint using the ideas from time-dependent regression models. In the second example, effect sizes were assumed to be heterogenous for different age strata. Since recruitment by strata differed from the originally intended composition of strata, we re-estimated the required sample sizes and showed that sufficient power could be obtained with an almost 25% lower sample size. This allowed successful completion the trial. In the third example, we did not choose the natural endpoint, which would be complete bone healing (yes/no), as primary endpoint. The non-inferiority margin would have been either large or the sample size would have been high with this endpoint. I therefore suggest a different approach to formal testing of non-inferiority involving both safety and function. In addition, I suggest to also demonstrate superiority using a surrogate endpoint. This endpoint makes use of specific properties of the novel material.

Conclusion: Statistical planning is crucial for making trials feasible. The active involvement of biostatisticians can lead to study designs, inclusion and exclusion criteria and endpoints that differ from the original ideas of stakeholders.

The author was CEO of StatSol and consultant to several medical device companies.

The authors declare that an ethics committee vote is not required.